2015/2015-vtalks_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis for vtalks.net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents:\n",
    "\n",
    "* [Introduction](#introduction)\n",
    "    * [Setup & Configuration](#setup-and-configuration)\n",
    "    * [Load the Data Set](#load-the-data-set)\n",
    "    \n",
    "* [Youtube Statistics Analysis](#youtube-statistics-analysis)\n",
    "    * [Youtube Views](#youtube-views)\n",
    "    * [Youtube Likes](#youtube-likes)\n",
    "    * [Youtube Dislikes](#youtube-dislikes)\n",
    "    * [Youtube Favorites](#youtube-favorites)\n",
    "    \n",
    "* [Statistics Analysis](#statistics-analysis)\n",
    "    * [Views](#views)\n",
    "    * [Likes](#likes)\n",
    "    * [Dislikes](#dislikes)\n",
    "    * [Favorites](#favorites)\n",
    "    \n",
    "* [Youtube Statistics Histograms](#youtube-statistics-histograms)\n",
    "    * [Youtube Views Histogram](#youtube-views-histogram)\n",
    "    * [Youtube Likes Histogram](#youtube-likes-histogram)\n",
    "    * [Youtube Dislikes Histogram](#youtube-dislikes-histogram)\n",
    "    * [Youtube Favorites Histogram](#youtube-favorites-histogram)\n",
    "    \n",
    "* [Statistics Histograms](#statistics-histograms)\n",
    "    * [Views Histogram](#views-histogram)\n",
    "    * [Likes Histogram](#likes-histogram)\n",
    "    * [Dislikes Histogram](#dislikes-histogram)\n",
    "    * [Favorites Histogram](#favorites-histogram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction <a class=\"anchor\" id=\"introduction\"></a>\n",
    "\n",
    "This jupyter network describes an exploratory data analysis for a data set of talks published on [vtalks.net](http://www.vtalks.net) website.\n",
    "\n",
    "We are going to use numpy and pandas to load and analyze our dataset, and we will use matplotlib python libraries for\n",
    "plotting the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/raul/Projects/vtalks/jupyter\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup & Configuration <a class=\"anchor\" id=\"setup-and-configuration\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling as pp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we configure matplotlib to ensure we have somne pretty plots :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-ticks',\n",
       " 'fivethirtyeight',\n",
       " 'seaborn-whitegrid',\n",
       " 'classic',\n",
       " '_classic_test',\n",
       " 'fast',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-pastel',\n",
       " 'grayscale',\n",
       " 'seaborn-notebook',\n",
       " 'ggplot',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-muted',\n",
       " 'seaborn',\n",
       " 'Solarize_Light2',\n",
       " 'seaborn-paper',\n",
       " 'bmh',\n",
       " 'seaborn-white',\n",
       " 'dark_background',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-deep']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "seaborn.set()\n",
    "plt.rc('figure', figsize=(16,8))\n",
    "plt.style.use('bmh')\n",
    "\n",
    "plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Data Set <a class=\"anchor\" id=\"load-the-dataset\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally load our dataset. Notice that there are different data sets available.\n",
    "\n",
    "The first one is a general data set with all the information available from the start (around mid 2010) until now. Then there are the same data sets but splitted by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# data_source = \"../.dataset/vtalks_dataset_2018.csv\"\n",
    "# data_source = \"../.dataset/vtalks_dataset_2017.csv\"\n",
    "# data_source = \"../.dataset/vtalks_dataset_2016.csv\"\n",
    "data_source = \"../.dataset/vtalks_dataset_2015.csv\"\n",
    "# data_source = \"../.dataset/vtalks_dataset_2014.csv\"\n",
    "# data_source = \"../.dataset/vtalks_dataset_2013.csv\"\n",
    "# data_source = \"../.dataset/vtalks_dataset_2012.csv\"\n",
    "# data_source = \"../.dataset/vtalks_dataset_2011.csv\"\n",
    "# data_source = \"../.dataset/vtalks_dataset_2010.csv\"\n",
    "# data_source = \"../.dataset/vtalks_dataset_all.csv\"\n",
    "\n",
    "data_set = pd.read_csv(\n",
    "    data_source,\n",
    "    parse_dates=[1],\n",
    "    dtype={\n",
    "        'id': int,\n",
    "        'youtube_view_count': int, \n",
    "        'youtube_like_count': int,\n",
    "        'youtube_dislike_count': int,\n",
    "        'youtube_favorite_count': int,\n",
    "        'view_count': int, \n",
    "        'like_count': int,\n",
    "        'dislike_count': int,\n",
    "        'favorite_count': int,\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                 int64\n",
       "created                   datetime64[ns]\n",
       "youtube_view_count                 int64\n",
       "youtube_like_count                 int64\n",
       "youtube_dislike_count              int64\n",
       "youtube_favorite_count             int64\n",
       "view_count                         int64\n",
       "like_count                         int64\n",
       "dislike_count                      int64\n",
       "favorite_count                     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data_set.dtypes"
   ]
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  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4954 entries, 0 to 4953\n",
      "Data columns (total 10 columns):\n",
      "id                        4954 non-null int64\n",
      "created                   4954 non-null datetime64[ns]\n",
      "youtube_view_count        4954 non-null int64\n",
      "youtube_like_count        4954 non-null int64\n",
      "youtube_dislike_count     4954 non-null int64\n",
      "youtube_favorite_count    4954 non-null int64\n",
      "view_count                4954 non-null int64\n",
      "like_count                4954 non-null int64\n",
      "dislike_count             4954 non-null int64\n",
      "favorite_count            4954 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(9)\n",
      "memory usage: 387.1 KB\n"
     ]
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   "source": [
    "data_set.info()"
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  {
   "cell_type": "code",
   "execution_count": 51,
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       "      <td>2017-12-22 01:48:33</td>\n",
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       "      id             created  youtube_view_count  youtube_like_count  \\\n",
       "0  16243 2017-12-28 00:02:02                 371                   9   \n",
       "1    552 2017-12-22 01:49:42                 588                   6   \n",
       "2    551 2017-12-22 01:49:22                 619                   8   \n",
       "3    550 2017-12-22 01:49:01                 329                   5   \n",
       "4    549 2017-12-22 01:48:33                 765                  13   \n",
       "\n",
       "   youtube_dislike_count  youtube_favorite_count  view_count  like_count  \\\n",
       "0                      0                       0           9           0   \n",
       "1                      0                       0         107           0   \n",
       "2                      0                       0         103           0   \n",
       "3                      0                       0         107           0   \n",
       "4                      2                       0          62           0   \n",
       "\n",
       "   dislike_count  favorite_count  \n",
       "0              0               0  \n",
       "1              0               0  \n",
       "2              0               0  \n",
       "3              0               0  \n",
       "4              0               0  "
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   "cell_type": "code",
   "execution_count": 52,
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       "      <th>youtube_dislike_count</th>\n",
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       "      <th>count</th>\n",
       "      <td>4954.000000</td>\n",
       "      <td>4.954000e+03</td>\n",
       "      <td>4954.000000</td>\n",
       "      <td>4954.000000</td>\n",
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       "      <td>4954.000000</td>\n",
       "      <td>4954.000000</td>\n",
       "      <td>4954.000000</td>\n",
       "      <td>4954.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>8520.422285</td>\n",
       "      <td>4.025551e+03</td>\n",
       "      <td>50.384941</td>\n",
       "      <td>2.952160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.756964</td>\n",
       "      <td>0.001817</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>0.001009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5885.866819</td>\n",
       "      <td>4.368997e+04</td>\n",
       "      <td>419.900709</td>\n",
       "      <td>58.493327</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.275571</td>\n",
       "      <td>0.042588</td>\n",
       "      <td>0.020091</td>\n",
       "      <td>0.031756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3048.250000</td>\n",
       "      <td>2.440000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7562.500000</td>\n",
       "      <td>6.845000e+02</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14621.750000</td>\n",
       "      <td>2.181750e+03</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>17791.000000</td>\n",
       "      <td>2.929460e+06</td>\n",
       "      <td>27165.000000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "                 id  youtube_view_count  youtube_like_count  \\\n",
       "count   4954.000000        4.954000e+03         4954.000000   \n",
       "mean    8520.422285        4.025551e+03           50.384941   \n",
       "std     5885.866819        4.368997e+04          419.900709   \n",
       "min        1.000000        0.000000e+00            0.000000   \n",
       "25%     3048.250000        2.440000e+02            2.000000   \n",
       "50%     7562.500000        6.845000e+02            8.000000   \n",
       "75%    14621.750000        2.181750e+03           26.000000   \n",
       "max    17791.000000        2.929460e+06        27165.000000   \n",
       "\n",
       "       youtube_dislike_count  youtube_favorite_count   view_count  \\\n",
       "count            4954.000000                  4954.0  4954.000000   \n",
       "mean                2.952160                     0.0    15.756964   \n",
       "std                58.493327                     0.0    14.275571   \n",
       "min                 0.000000                     0.0     0.000000   \n",
       "25%                 0.000000                     0.0     8.000000   \n",
       "50%                 0.000000                     0.0    11.000000   \n",
       "75%                 2.000000                     0.0    17.000000   \n",
       "max              4066.000000                     0.0   148.000000   \n",
       "\n",
       "        like_count  dislike_count  favorite_count  \n",
       "count  4954.000000    4954.000000     4954.000000  \n",
       "mean      0.001817       0.000404        0.001009  \n",
       "std       0.042588       0.020091        0.031756  \n",
       "min       0.000000       0.000000        0.000000  \n",
       "25%       0.000000       0.000000        0.000000  \n",
       "50%       0.000000       0.000000        0.000000  \n",
       "75%       0.000000       0.000000        0.000000  \n",
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     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data_set.describe()"
   ]
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   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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       "\n",
       "        .indent {\n",
       "            margin-left: 1em;\n",
       "        }\n",
       "\n",
       "        .center-img {\n",
       "            margin-left: auto !important;\n",
       "            margin-right: auto !important;\n",
       "            display: block;\n",
       "        }\n",
       "\n",
       "        /* Table example_values */\n",
       "            table.example_values {\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "            .example_values th {\n",
       "                border: 0;\n",
       "                padding: 0 ;\n",
       "                color: #555;\n",
       "                font-weight: 600;\n",
       "            }\n",
       "\n",
       "            .example_values tr, .example_values td{\n",
       "                border: 0;\n",
       "                padding: 0;\n",
       "                color: #555;\n",
       "            }\n",
       "\n",
       "        /* STATS */\n",
       "            table.stats {\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "            .stats th {\n",
       "                border: 0;\n",
       "                padding: 0 2em 0 0;\n",
       "                color: #555;\n",
       "                font-weight: 600;\n",
       "            }\n",
       "\n",
       "            .stats tr {\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "            .stats td{\n",
       "                color: #555;\n",
       "                padding: 1px;\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* Sample table */\n",
       "            table.sample {\n",
       "                border: 0;\n",
       "                margin-bottom: 2em;\n",
       "                margin-left:1em;\n",
       "            }\n",
       "            .sample tr {\n",
       "                border:0;\n",
       "            }\n",
       "            .sample td, .sample th{\n",
       "                padding: 0.5em;\n",
       "                white-space: nowrap;\n",
       "                border: none;\n",
       "\n",
       "            }\n",
       "\n",
       "            .sample thead {\n",
       "                border-top: 0;\n",
       "                border-bottom: 2px solid #ddd;\n",
       "            }\n",
       "\n",
       "            .sample td {\n",
       "                width:100%;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* There is no good solution available to make the divs equal height and then center ... */\n",
       "            .histogram {\n",
       "                margin-top: 3em;\n",
       "            }\n",
       "        /* Freq table */\n",
       "\n",
       "            table.freq {\n",
       "                margin-bottom: 2em;\n",
       "                border: 0;\n",
       "            }\n",
       "            table.freq th, table.freq tr, table.freq td {\n",
       "                border: 0;\n",
       "                padding: 0;\n",
       "            }\n",
       "\n",
       "            .freq thead {\n",
       "                font-weight: 600;\n",
       "                white-space: nowrap;\n",
       "                overflow: hidden;\n",
       "                text-overflow: ellipsis;\n",
       "\n",
       "            }\n",
       "\n",
       "            td.fillremaining{\n",
       "                width:auto;\n",
       "                max-width: none;\n",
       "            }\n",
       "\n",
       "            td.number, th.number {\n",
       "                text-align:right ;\n",
       "            }\n",
       "\n",
       "        /* Freq mini */\n",
       "            .freq.mini td{\n",
       "                width: 50%;\n",
       "                padding: 1px;\n",
       "                font-size: 12px;\n",
       "\n",
       "            }\n",
       "            table.freq.mini {\n",
       "                 width:100%;\n",
       "            }\n",
       "            .freq.mini th {\n",
       "                overflow: hidden;\n",
       "                text-overflow: ellipsis;\n",
       "                white-space: nowrap;\n",
       "                max-width: 5em;\n",
       "                font-weight: 400;\n",
       "                text-align:right;\n",
       "                padding-right: 0.5em;\n",
       "            }\n",
       "\n",
       "            .missing {\n",
       "                color: #a94442;\n",
       "            }\n",
       "            .alert, .alert > th, .alert > td {\n",
       "                color: #a94442;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* Bars in tables */\n",
       "            .freq .bar{\n",
       "                float: left;\n",
       "                width: 0;\n",
       "                height: 100%;\n",
       "                line-height: 20px;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                background-color: #337ab7;\n",
       "                border-radius: 3px;\n",
       "                margin-right: 4px;\n",
       "            }\n",
       "            .other .bar {\n",
       "                background-color: #999;\n",
       "            }\n",
       "            .missing .bar{\n",
       "                background-color: #a94442;\n",
       "            }\n",
       "            .tooltip-inner {\n",
       "                width: 100%;\n",
       "                white-space: nowrap;\n",
       "                text-align:left;\n",
       "            }\n",
       "\n",
       "            .extrapadding{\n",
       "                padding: 2em;\n",
       "            }\n",
       "\n",
       "            .pp-anchor{\n",
       "\n",
       "            }\n",
       "\n",
       "</style>\n",
       "\n",
       "<div class=\"container pandas-profiling\">\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Overview</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Dataset info</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Number of variables</th>\n",
       "                <td>10 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Number of observations</th>\n",
       "                <td>4954 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total Missing (%)</th>\n",
       "                <td>0.0% </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total size in memory</th>\n",
       "                <td>387.1 KiB </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Average record size in memory</th>\n",
       "                <td>80.0 B </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Variables types</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Numeric</th>\n",
       "                <td>3 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Categorical</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Boolean</th>\n",
       "                <td>3 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Date</th>\n",
       "                <td>1 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Text (Unique)</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Rejected</th>\n",
       "                <td>3 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Unsupported</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
       "        \n",
       "        <p class=\"h4\">Warnings</p>\n",
       "        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_youtube_dislike_count\"><code>youtube_dislike_count</code></a> is highly correlated with <a href=\"#pp_var_youtube_like_count\"><code>youtube_like_count</code></a> (ρ = 0.94612) <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_youtube_favorite_count\"><code>youtube_favorite_count</code></a> has constant value 0 <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_youtube_like_count\"><code>youtube_like_count</code></a> is highly correlated with <a href=\"#pp_var_youtube_view_count\"><code>youtube_view_count</code></a> (ρ = 0.98338) <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_youtube_view_count\"><code>youtube_view_count</code></a> is highly skewed (γ1 = 61.064)  <span class=\"label label-info\">Skewed</span></li> </ul>\n",
       "    </div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Variables</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_created\">created<br/>\n",
       "            <small>Date</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>4114</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>83.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>2017-01-03 12:12:13</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>2017-12-28 00:02:02</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram5668047025301993052\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAABSxJREFUeJzt3E1IVGsYB/D/0UtkQdBiHBEXDcjkqlwMrmay1eDHpCioGREtahGIkpn50SAupkUoCH4thxYKCrMS5LYQIfCjuBSm0Ie4UQOnQQn8IJV87uJyhytOjzfPOHPS/283ntf3vK88//O%2BZ2aOhogIiCimlGQPgMjK/kj2AH4nrtY/f6n9X4GCYxoJJQpXECIFA0KkYECIFLwHOYV%2B9V4KOL33U1xBiBQMCJGCASFSMCBECgaESMGAECkYECIFA0Kk4AeFFsMP8ayFKwiRggEhUnCLRf/Lad36MSB0bE5CqLjFIlKc2hXkKFc3On1ObUASgSH8/XGLRaRgQIgUDAiRggEhUjAgRAoGhEjBgBApGBAiBQNCpGBAiBQMCJGCASFSMCBECgaESMGAECkYECIFA0Kk4BOFJwCfXDw%2BXEGIFCdiBeEVlI4LVxAiBQNCpLDcFovbJbISriBECgaESMGAECkMEZFkDwIAvn79iqGhIVRVVSE9PT3Zw6EksVodWGYFiUQi6OnpQSQSSfZQKImsVgeWCQiRFTEgRAoGhEhhmYDYbDbU1NTAZrMleyiURFarA8u8i0VkRZZZQYisiAEhUjAgRAoGhEjBgBApGBAiBQNCpDAdkGAwCJ/PB5/Ph%2BbmZuzs7ODz58%2BorKxEQUEBamtrsbW1te93QqEQmpqaoq/v37%2BP0tJSlJaW4saNG7h8%2BTKmpqYOnGt3dxctLS0oKipCcXExZmZm9h0Ph8Nwu91mp0RHEI862NnZwaNHj%2BDz%2BVBSUoLJycmY50poHYgJMzMz4vP5ZHNzU/b29qShoUGCwaCUlJTI69evRUSkq6tLOjo6RETk%2B/fv8vz5c8nNzZUnT57E7LO/v1/q6%2BtjHgsGg9LU1CQiIvPz8%2BL1emV3d1dERMbHx8Xr9YrT6TQzJTqCeNXB8PCw1NbWiojIx48fxePxxDxfIuvA1Apy4cIF%2BP1%2BnDt3DoZhICcnB58%2BfcL6%2Bjry8vIAABUVFRgdHQUATE9PQ0Tw%2BPHjmP0tLS1hcHAQT58%2BjXl8fHwcZWVlAIDs7GzY7Xa8e/cOADA8PIzu7m4z06Ejilcd7O3tYXt7Gz9%2B/MD29jbOnj0b83yJrANTAbl06VL0D7C6uoqBgQE4HA7Y7fZom/T0dITDYQBAfn4%2BGhsbfzrx/v5%2B3L17FxcvXox5PBwOH%2Bh7ZWUFANDX1wen02lmOnRE8aqDsrIyfPv2DR6PB7du3UJDQ0PM8yWyDuJyk768vIw7d%2B6goqICLpfrwHHDMA7tY2NjA2NjY6iqqvppG4nxtbGUFL7PYBVm66Cnpwe5ubmYmJjAyMgIAoEAvnz5cqBdIuvAdK8fPnxAdXU1bt68iQcPHiAjI2Pf02CRSAQZGRmH9vPq1Su43W6cP38%2B%2BrPW1tbozfvs7CzsdvuBvv97JaHkiUcdjI2Noby8HIZhwOFw4OrVq3j//n1S68DU/8VaW1vDvXv30NbWBq/XCwDIzMxEWloa3rx5g7y8PIRCIeTn5x/a19u3b6PL9L8CgcC%2B19evX0coFILL5cLCwgIWFxdx5coVM1OgOIhXHeTk5ODly5dwOp1YW1vD3NwcHj58iMLCwn3tElkHpgLy4sULbGxsoLe3F729vQD%2BGXxnZyf8fj/W19eRlZWFzs7OQ/taXFzEtWvX1Da3b99Ge3s7iouLYRgGnj17hjNnzpiZAsVBvOqgubkZfr8fRUVFSE1NRV1dHRwOx4F2iawDPg9CpOAdLpGCASFSMCBEir8Bifq4J89RCK8AAAAASUVORK5CYII%3D\">\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives5668047025301993052,#minihistogram5668047025301993052\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives5668047025301993052\">\n",
       "    <img 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ARI0boxIkTFpYGAABgT4Gvvvqq2rVrp0ceecSzcOvWrWrVqpUyMjIsLA0AAMCeAvfu3asXXnhBkZGRnoXNmzfXlClTtGfPHgtLAwAAsCeHw%2BFQWVlZjRUVFRU80R03NDv9DS8AwPUlsH///nrllVf0j3/8w7OwsLBQGRkZSkhIsLA0AAAAe3JMnTpVjzzyiIYOHarw8HBJUllZmbp27aq0tDSLywMAALAfR0REhDZu3Khdu3apoKBADodDt99%2Bu%2B666y4FBARYXR8AAIDtOCQpKChICQkJ3BIEAAAwwOFyubRo0SLt27dP58%2Bfr/HG9o8//tii0gAAAOzJ8Zvf/EYHDhzQvffeq5tvvtnqegAAAGzPsWfPHq1YsUJxcXFW1wLgBsEjMAD4u8CwsDBFRERYXQcAAIDfCBwxYoRWrFihCxcuWF0LAACAX3CUlpZqy5Yt%2Bu///m%2B1bdtWwcHBXhu8%2B%2B67FpUGAABgTw5JGj58uNV1AAAA%2BA1HRkaG1TUAAAD4lUBJKi4uVmZmpp577jn93//9n7Zt26YjR45YXRsAAIAtBR49elT/9m//po0bN%2BrDDz9UeXm5tm7dqlGjRik/P9/q%2BgAAAGwncM6cORoyZIg%2B%2Bugj3XTTTZKkhQsXatCgQVqwYIHF5QEAANhP4L59%2B/TII494/WFnh8Ohp556SgcPHrSwNAAAAHsKrK6uVnV1dY0VP/zwg4KCgiwoCQAAwN4C%2B/Xrp2XLlnmFrNLSUs2fP199%2BvSxsDQAAAB7CnzhhRd04MAB9evXT%2BfOndOTTz6pu%2B%2B%2BW8eOHdPUqVOtrg8AAMB2HC1bttQHH3ygLVu26G9/%2B5uqq6s1duxYjRgxQk2aNLG6PgAAANtxSFJoaKjGjBljdS0AAAB%2BwTF%2B/PgrbsDfIgQAAKgbR3R0tNeCqqoqHT16VN98840eeughi8oCAACwr1r/FmFWVpaKioqucTkAAAD2F1jbihEjRuhPf/rTtawFAADAL9QasP73f/%2BXB40CAADUw2Xf5H7mzBl9/fXX%2Bo//%2BA8LSgIAALA3R%2BvWrb3%2BDqEk3XTTTXrwwQd13333WVQWAACAfTnmzJljdQ0AAAB%2BxfHFF1/4vHHv3r1/wlIAAAD8g2PcuHGeW4Rut9uz4tJlAQEB%2Btvf/nbtKwQAALAZx5tvvqlXXnlFzz//vOLj4xUcHKwvv/xSM2fO1C9/%2BUvdc889VtcIAABgK4EZGRmaPn26hg4dqmbNmqlx48bq06ePZs6cqdWrVys6OtrzAQAAgKsLLC4uvmx4atKkiU6fPm1BSQAAAPYW2LNnTy1cuFBnzpzxLCwtLdX8%2BfN11113WVgaAACAPTmmTZum8ePHq3///mrfvr3cbrf%2B/ve/KzIyUu%2B%2B%2B67V9QEAANiOo0OHDtq6dau2bNmiw4cPS5IeeOAB3XvvvQoNDbW4PAAAAPtxSFLTpk01ZswYHTt2TG3btpX049PcAQAAUHeBbrdbCxYsUO/evTV8%2BHAVFRVp6tSpSk9P1/nz562uDwAAwHYCV65cqU2bNmnGjBkKDg6WJA0ZMkQfffSRMjMz6zXohAkT9MILL3heHzx4UGPGjJHT6dSoUaN04MABr%2B23bNmiIUOGyOl0KiUlRadOnar/EQEAAFgsMCcnR9OnT1dSUpLn6e333HOPXnnlFf3hD3%2Bo84B//OMf9cknn3hel5eXa8KECYqLi9OGDRsUExOjiRMnqry8XJK0f/9%2Bpaena9KkScrJyVFZWZnS0tLMHB0AAIAFAo8dO6bOnTvXWNGpUye5XK46DVZaWqp58%2Bape/funmVbt25VSEiIpkyZog4dOig9PV2NGzfWtm3bJEmrVq1SYmKiRo4cqU6dOmnevHn65JNPVFhY2LAjAwAAsEhgdHS0vvzyyxorPv30U88b3n01d%2B5cjRgxQrfffrtnWX5%2BvmJjYz1XxwICAtSrVy/l5eV51sfFxXm2b9WqlVq3bq38/Pz6HA8AAIDlAh977DG9/PLLevfdd%2BV2u7V7924tWLBA8%2BbN07hx43weaPfu3dq7d6%2Beeuopr%2BUul0tRUVFeyyIiIlRUVCRJKi4uvuJ6AAAAu3GMGjVKVVVVWrp0qc6ePavp06erefPmeuaZZzR27FifBjl37pxmzJih6dOnq1GjRl7rKioqPG%2Bevyg4OFiVlZWSpLNnz15xvR0VFxfXuL3qcITVCJL1ERQU6PUvvNEfQHI4rJn/nH9oiEvnrd3nk2PLli0aNmyYkpOTderUKbndbkVERNRpkMzMTHXr1k0JCQk11oWEhNQIS5WVlZ4gVtt6Oz/kNCcnp8ZvYKakpCg1NdXYPsLD7dufa4H%2B4EbWrFljS/fP%2BYf6qG3e2nU%2BOWbOnKnf//73atq0qZo3b16vQf74xz%2BqpKREMTExkuQJTB9%2B%2BKGGDx%2BukpISr%2B1LSko8V3Natmx52fWRkZH1quV6kJycrEGDBnktczjCdPr0Dw0eOygoUOHhoSorq9CFC9UNHs/f0B9ARr7X1AfnHxri0nlraj5Z9QOHo3379vrmm2%2B83pheVytXrlRVVZXn9YIFCyRJv/71r/XFF19o%2BfLlcrvdCggIkNvt1r59%2B/TEE09IkpxOp3Jzc5WUlCRJOnHihE6cOCGn09mAw7JWVFRUjduBLtf3qqoy9w3nwoVqo%2BP5G/qDG5nVc5/zD/VR25yx63xydOrUSb/%2B9a%2B1YsUKtW/fXiEhIV4bZGRkXHWQ6Ohor9eNG/%2BYFtu1a6eIiAi99tprmj17tu6//36tWbNGFRUVSkxMlCSNHTtW48aNU8%2BePdW9e3fNnj1bAwcOrPNvMAIAAFwvHN9%2B%2B61iY2Mlqc7PvfJFkyZNtGzZMs2YMUPvv/%2B%2BOnbsqOzsbIWFhUmSYmJiNHPmTL3xxhv67rvv1LdvX82aNct4HQAAANeKY%2BXKlcYHnTNnjtfrHj16aOPGjbVun5SU5LlFCAAAYHee332cMGGCiouLrawFAADAL3gC1hdffKFz585ZWQsAAIBfsOfTuwAAAK5jBCwAAADDCFgAAACGOdLS0iRJ58%2Bf1/z58z3PsLrIl%2BdgAQAA4P/nOHbsmKQfn0d1%2BvRpnT592uKSAAAA7O0neQ4WAADAjYz3YAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAzz24B18uRJpaamKj4%2BXgkJCcrIyNC5c%2BckSYWFhXr44YfVs2dP3XPPPdqxY4fX5%2B7atUvDhw%2BX0%2BnU%2BPHjVVhYaMUhAAAAm/LLgOV2u5WamqqKigq99957ev3117V9%2B3YtWrRIbrdbKSkpatGihdavX68RI0Zo0qRJOn78uCTp%2BPHjSklJUVJSktatW6fmzZvrqaeektvttvioAACAXTisLuCncOTIEeXl5Wnnzp1q0aKFJCk1NVVz585V//79VVhYqDVr1igsLEwdOnTQ7t27tX79ek2ePFlr165Vt27d9Oijj0qSMjIy1LdvX33%2B%2Bee68847rTwsAABgE355BSsyMlIrVqzwhKuLzpw5o/z8fHXp0kVhYWGe5bGxscrLy5Mk5efnKy4uzrMuNDRUXbt29awHAAC4Gr8MWOHh4UpISPC8rq6u1qpVq9SnTx%2B5XC5FRUV5bR8REaGioiJJuup6AACAq/HLW4SXmj9/vg4ePKh169bp7bffVnBwsNf64OBgVVZWSpIqKiquuN4XxcXFcrlcXsscjrAawa0%2BgoICvf6FN/oDSA6HNfOf8w8Ncem8tft88vuANX/%2BfL3zzjt6/fXXdccddygkJESlpaVe21RWVqpRo0aSpJCQkBphqrKyUuHh4T7vMycnR5mZmV7LUlJSlJqaWs%2BjqCk8PNTYWP6I/uBG1qxZY0v3z/mH%2Bqht3tp1Pvl1wJo1a5ZWr16t%2BfPna%2BjQoZKkli1b6tChQ17blZSUeK4utWzZUiUlJTXWd%2B7c2ef9Jicna9CgQV7LHI4wnT79Q30Ow0tQUKDCw0NVVlahCxeqGzyev6E/gIx8r6kPzj80xKXz1tR8suoHDr8NWJmZmVqzZo0WLlyoYcOGeZY7nU5lZ2fr7NmznqtWubm5io2N9azPzc31bF9RUaGDBw9q0qRJPu87Kiqqxu1Al%2Bt7VVWZ%2B4Zz4UK10fH8Df3Bjczquc/5h/qobc7YdT7Z88bmVRw%2BfFhLlizR448/rtjYWLlcLs9HfHy8WrVqpbS0NBUUFCg7O1v79%2B/X6NGjJUmjRo3Svn37lJ2drYKCAqWlpalNmzY8ogEAAPjMLwPWxx9/rAsXLmjp0qXq16%2Bf10dQUJCWLFkil8ulpKQkbd68WVlZWWrdurUkqU2bNlq8eLHWr1%2Bv0aNHq7S0VFlZWQoICLD4qAAAgF345S3CCRMmaMKECbWub9eunVatWlXr%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%2BW1zOEIU1RUVIPrCwoK9PoXAAB/4HB4/79m9//vCFiXqKio8ApXkjyvKysrfRojJydHmZmZXssmTZqkyZMnN7i%2B4uJivfPOCiUnJysqKkp7Z3Pb8p8VFxcrJyfH0x9cHn3yDX2qG/rlG/rkm0v/v7Mbe8bCn1BISEiNIHXxdaNGjXwaIzk5WRs2bPD6SE5ONlKfy%2BVSZmZmjStk%2BBH98Q198g19qhv65Rv65Bu794krWJdo2bKlTp8%2BraqqKjkcP7bH5XKpUaNGCg8P92mMqKgoW6ZtAABgBlewLtG5c2c5HA7l5eV5luXm5qp79%2B4KDKRdAADg6kgMlwgNDdXIkSP10ksvaf/%2B/froo4/029/%2BVuPHj7e6NAAAYBNBL7300ktWF3G96dOnjw4ePKjXXntNu3fv1hNPPKFRo0ZZXZZH48aNFR8fr8aNG1tdynWJ/viGPvmGPtUN/fINffKNnfsU4Ha73VYXAQAA4E%2B4RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAANMjJkyeVmpqq%2BPh4JSQkKCMjQ%2BfOnZMkFRYW6uGHH1bPnj11zz33aMeOHZcdY/PmzRo3bpzn9bFjx9SxY8fLfnzxxRe11rJr1y4NHz5cTqdT48ePV2FhYYPGA4D64jENAOrN7Xbr/vvvV3h4uKZMmaLvvvtOL774ogYPHqwpU6ZoxIgRuuOOO/Tkk0/qo48%2B0tKlS7V161a1bt3aM8aePXv0xBNPqHv37lq5cqUk6cKFCzp16pTXvubMmaOjR49q9erVuummm2rUcvw%2BHFTJAAAKlklEQVT4cd17772aPHmyEhISlJWVpcOHD2vz5s2qrq6u83gA0BD8LUIA9XbkyBHl5eVp586datGihSQpNTVVc%2BfOVf/%2B/VVYWKg1a9YoLCxMHTp00O7du7V%2B/XpNnjxZkpSZmally5apffv2XuMGBQUpMjLS83rfvn368MMPtWnTplrD0Nq1a9WtWzc9%2BuijkqSMjAz17dtXn3/%2Bue688846jwcADcEtwiuww62PS02bNk2LFy9uwFH/9OzS1/Lyck2bNk133nmnevfurd/85jf64YcfDHXh6uzQp8jISP3nf/6n%2Bvbt6xnnueeeU3FxsfLz89WlSxeFhYV5xomNjfX6O587d%2B7UW2%2B9pV/84hdX7MVrr72mf//3f1eHDh1q3SY/P19xcXGe16GhoeratavX/uoy3o3Cl7n0U8%2BjK811K/hSz44dO3TfffcpJiZGDz/8sI4cOWJRtfZ36fyyypYtWzRkyBB17NhRKSkp6tSpU4PmIwGrFm63W6mpqaqoqNB7772n119/Xdu3b9eiRYvkdruVkpKiFi1aaP369RoxYoQmTZqk48ePe42xZ88eTZ8%2B3WtZq1attGPHDq%2BP4cOHq3v37urZs%2Bdlazl%2B/LhSUlKUlJSkdevWqXnz5nrqqad06d3d5cuXa%2B3atWYbYZid%2Bvrqq6/qwIEDeuutt/T2229r//79mjNnzk/TmEvYpU/h4eGKjIzULbfcoh07dujTTz9V9%2B7d1a9fP7lcLkVFRXmNFRERoaKiIs/r1atXKz4%2B/oq9yM3NVV5eniZOnHjF7XzZX13GuxH4MpeuxTyqba5bwZd6CgoKNHHiRA0ePFjr169Xly5d9NBDD13TH8D8xeXmlxX279%2Bv9PR0paSkKDw8XDt27FDv3r0bNh/duKxDhw6577jjDrfL5fIs%2B8Mf/uDu16%2Bfe9euXe6ePXu6f/jhB8%2B6hx56yP3GG294Xi9evNjdrVs39/Dhw90PPvhgrfvJzc11d%2B3a1X3o0KFat1m0aJHXGOXl5e6YmBj3nj173G632/3999%2B7J0%2Be7O7du7d7wIABXnVcb%2BzU15deesm9d%2B9ez/p33nnHnZiYWLcDric79en99993Jycnu91ut3vOnDnu7t27u7/%2B%2Bmt3Wlqae8qUKV5jrV271j1kyJAa%2B3jjjTdqrXPKlCnup59%2Butb6Lho8eLB73bp1Xsuef/5594svvliv8W4EV5tL12Ie/f73v691rlvhSufeRS%2B//LL7gQce8Lyurq52JyYmulevXn1Na7U7X%2BfXtfD888%2B7p06d6vn6Hzt2zN2xY0f3P/7xj3rPR65g1SIyMlIrVqzwvK/kojNnzlx3tz6OHTumc%2BfOacOGDWrbtm2djvNas1NfZ8yYodjYWEk/9njLli1Xvdpiip36dOjQIbVv317z58/XO%2B%2B8o/nz5%2BuOO%2B5QSEiIKisrvcaqrKxUo0aNrt6A/6eqqkoff/yx7rvvPq/l06dPV0xMjOfj%2BPHjte4vNDT0quPdqK42l67FPCorK6t1rlvhSufeRYWFherRo4fndUBAgO64447L3o5G7XydX9fCxfl58esfHR2t1q1bKz8/X1L95iNvcq9FeHi4EhISPK%2Brq6u1atUq9enTx%2BdbH5L02Wef1bqPi7cqFi5ceMVarra/Tp06admyZb4dmMXs1NeLpk6dqg8%2B%2BEDR0dFKSUm58gEaYqc%2BHT58WAUFBSoqKlJ4eLi2bdummJgYtWzZUocOHfL6vJKSkhpjXUleXp6qqqrUt29fr%2BVPP/20HnvsMc/rqKgotWzZUiUlJTX217lz56uOd6O62tf2Ws2jf75d%2B89z3QpXOvcuatGihU6ePOn1eUVFRWratOk1q9Mf%2BDK/rpXi4mJFRUV5ff0jIiJ0/Phx/eUvf6nXfOQKlo/mz5%2BvgwcP6tlnn1VFRYWCg4O91gcHB9f46flq3n//ff385z9Xy5Ytr7idqf1dj%2BzQ18cff1w5OTmKjo7W448/rurq6jrVY8L13Ke8vDwVFRXp6aef1tKlS1VcXOx57MJf//pXnT171vN5ubm5cjqdPteYn5%2Bvrl27KiQkxGt5RESE2rVr5/lwOBxyOp3Kzc31qvvgwYNe%2B6ttvBuViblk%2Bnz757l%2BPbhcPYmJifrwww%2B1fft2VVVVaePGjfryyy91/vx5CytFQ5w9e/ay83PRokX1no8ELB/Y4daHHdmlr7fffrt69uyp119/XV9//fU1fzDl9dynw4cPq7y8XI8//rjGjBmjdu3aacaMGTp48KBCQkLUqlUrpaWlqaCgQNnZ2dq/f79Gjx7tc50FBQU%2B/6bfqFGjtG/fPmVnZ6ugoEBpaWlq06aN7rzzznqNdyNo6Fwyfb5dOtetVls9/fv3V0pKilJTU9W9e3dt2rRJI0aMUJMmTYzt%2B8033/Tq4d69e42NjZpqm5/V1dX1no/cIryKWbNmafXq1Zo/f76GDh0qSdflrQ%2B7ud77WllZqe3bt6tv376eb5otWrTQLbfcotOnT9fpWBvieu/Txx9/rAsXLmj58uVavny51zYul0tLlixRenq6kpKS1K5dO2VlZXk9ZPRq6jLP27Rpo8WLF%2BvVV19VVlaWYmJilJWVpYCAgHqNdyNo6Fwy%2BX3scnPdSler58knn9Rjjz2m77//XhEREXr66acVHR1tbP/333%2B/EhMTPa%2BvdoUQDXPp/Jw1a5YOHDigBQsW1Hs%2BErCuIDMzU2vWrNHChQs1bNgwz3Kn06ns7GydPXvW85Nebm6u5w3RvrjSrY%2BIiAivZbXd%2Bpg0aVJ9DstyduhrYGCgXnjhBc2aNUvDhw%2BX9OOvmZ8%2BffqaXQGxQ5%2B6deum5cuXa/HixZ73KJw8eVIDBgzQbbfdpnbt2mnVqlVXrefig0cvtWLFCp%2BPSZIGDBigAQMG1Lq%2BruP5u4bOJVPzqLa5bpWr1bNlyxbl5%2BcrPT1dEREROnv2rD777DOjj3G55ZZbdMsttxgbD1d2cX4mJSUpMzNTq1atUkBAQJ3e0nApbhHW4vDhw1qyZIkef/xxxcbGyuVyeT7i4%2BOvu1sfdmGXvjocDiUnJ2vhwoXau3evDhw4oGeffVaDBw/Wv/7rv9b38H1mlz41adJEsbGxysjI0P79%2B/XXv/5Vzz77rBISEtSxY8f6Hj6ukYbOJRPzKDIysta5boUrnXsXtW/fXmvWrNF//dd/6e9//7uee%2B45tWrVSv3797ekZjTc2LFjtWnTJr333nvKzMxUfHy8fvazn6lRo0b1no8ErFpcvPWxdOlS9evXz%2BsjKChIS5YskcvlUlJSkjZv3lyvWx%2B%2B/sbJxVsf69ev1%2BjRo1VaWlrj1odd2Kmvv/rVrzR06FA988wzGj9%2BvG699VbNnTu3XsddV3bq09y5c9WlSxdNmDBB48aNU3R0tBYsWFCv48a11dC5ZGIeXWmuW8GXerp166aXXnpJc%2BbMUVJSkiRp2bJlCgzkv1S7iomJ0cyZM5WRkSG3263PP/9cO3fubNB85I89AwAAGEbcBgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD/j8JGbeCvIP2dQAAAABJRU5ErkJggg%3D%3D\">\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_dislike_count\">dislike_count<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.00040371</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable8400717083172003599\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 100.0%\">\n",
       "            4952\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.0%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        2\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable8400717083172003599, #minifreqtable8400717083172003599\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable8400717083172003599\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">4952</td>\n",
       "        <td class=\"number\">100.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_favorite_count\">favorite_count<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.0010093</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable-4399898176305375412\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 99.9%\">\n",
       "            4949\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.1%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        5\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-4399898176305375412, #minifreqtable-4399898176305375412\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-4399898176305375412\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">4949</td>\n",
       "        <td class=\"number\">99.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_id\">id<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>4954</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>100.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>8520.4</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>1</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>17791</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-686803840885803429\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAATZJREFUeJzt2stNAzEUQFGCUhJF0BNrekoR9GT2CF1lkIax7HP2kbyYG3/0bmOM8QL86vXqBcDM7lcv4Ke3j8fh33x9vp%2BwErCDQBIIBIFAEAgEgUAQCASBQBAIBIFAEAiE6UZNmNOuI0B2EAgCgSAQCEvcQXY9H3O%2BJQJhTiv8cTliQRAIBIFAEAgEl/QDjl46Z7twcpxANvSX16VdbRuIj4RnuINAEAgEgUAQCASBQNj2Fes/rDCstzuBLMCT9XkcsSAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCCY5p2Mydy52EEgCATCbYwxrl4EzMoOAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAuEbNGYgPS/kpCwAAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-686803840885803429,#minihistogram-686803840885803429\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-686803840885803429\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-686803840885803429\"\n",
       "                                                  aria-controls=\"quantiles-686803840885803429\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-686803840885803429\" aria-controls=\"histogram-686803840885803429\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-686803840885803429\" aria-controls=\"common-686803840885803429\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-686803840885803429\" aria-controls=\"extreme-686803840885803429\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-686803840885803429\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>539.65</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>3048.2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>7562.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>14622</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>17020</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>17791</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>17790</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>11574</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>5885.9</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.6908</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.5247</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>8520.4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>5353.6</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>0.096388</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>42210172</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>34643000</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>38.8 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-686803840885803429\">\n",
       "            <img 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dv366xY8cqJiZG1113nbZu3eqjSgEAQGNx1he5d%2BrUSd27d1ePHj0kSdnZ2brllls0cuRI5eXl1VuBvpaenq5jx44pKytLU6dO1e9//3tt3rxZknTixAndfPPN6tevn1auXKm4uDhNmTJFJ06c8HHVAADAl%2BoMWDk5OXrggQeUmJiou%2B%2B%2BW8YYPfXUU1qzZo3Wrl2rrl276rbbbmuIWhvcF198oTVr1uihhx5SZGSkxo4dq1GjRumVV16RJP3tb39TSEiIpk%2Bfrq5du%2Bq%2B%2B%2B5Ty5Yt9e677/q4cgAA4Eu1XuQ%2BdOhQffnll%2BrVq5d%2B97vfaeTIkbrgggs801u2bKnhw4dr3bp19V6oL7hcLnXs2FGdO3f2jMXHx2vx4sWe6fHx8XI4HJIkh8Ohvn37Kjc3V8nJyT6pGQAA%2BF6tASspKUnJycnq3r37GecZMGCA3nvvPeuFNQZut1sRERFeY23bttWhQ4c807t161Zt%2Bs6dOxusRgAA0PjUGrDS09PrXEB4eLi1Yhqb4uJiBQcHe40FBwerrKys1ulFRUXatm2b17jT2aJaWDtfzZpxn1j4J6ez7vdu1fs7kN7ngdgzUOVsjgv%2BpNaAlZ%2BfrwcffFA5OTk6efJktemffvppvRXWGISEhHjCVJWysjKFhobWOv3rr7%2Bu9hVhamqq0tLS6rdgwE%2B0adPyrOcNDw%2Brx0oap0DsGTiX44I/qDVgPfDAA/r666911113eV17FSg6dOigwsJCr7HCwkK1b9%2B%2B1ul9%2BvTRY4895jXudLbQ4cPHrdbHb7nwV2ezLzRrFqTw8DAdPVqsiorKBqjK9wKxZ6CK7c/IKr4KbrUGLJfLpVdffVVRUVENVU%2BjEhsbq6%2B%2B%2BkoHDx7UD37wA0nf3Z4iNjZWkhQTE6Onn35axhg5HA4ZY5STk6Pf/va31daZ231M5eUcMAFJ57QvVFRUBty%2BE4g9A03tPV/rKZA2bdqoefPmDVVLo3PJJZd4bk%2BRl5en5cuXa9WqVRo/frwkadiwYTp69KhmzZqlXbt2adasWSouLtbw4cN9XDkAAPClWgPWhAkT9NhjjwX0n8aZPXu2WrZsqV/%2B8pdatGiRHn74YfXp00fSd3e6X7x4sbKzs5WcnCyXy6UlS5aoRYsWPq4aAAD4Uq1fEX744Yf6%2BOOP1b9/f7Vt27ba/5j75z//Wa/FNQZt27bVokWLzji9T58%2BeuONNxqwIgAA0NjVGrDi4%2BMVHx/fULUAAAA0CbUGrFtvvbWh6gAAAGgyag1Yb775Zq1PHj16tNViAAAAmoJaA9a9995b43hISIh%2B8IMfELAAAABqUGvAysvL83pcUVGhvXv3aubMmUpJSanXwgAAAPzVOd0KvFmzZuratavS09P1xBNP1FdNAAAAfu17/a2VoKAgFRQU2K4FAACgSTjni9yLioq0bNkyz802AQAA4O2cL3J3Op2Ki4vTzJkz66smAAAAv3ZOF7kDAACgbt/rGiwAAACcWa1nsJKSkuRwOM5qQYHwdwkBAADORq0Ba9y4cZo/f76uuOIK9e/fX8HBwdqyZYv%2B%2Bte/atSoUerUqVND1QmgCRk%2Bb52vSzgn79yW4OsSAPiZWgNWdna2br755mp/k7BPnz5avXq1Hn744XotDgAAwB/Veg3Wxo0bNXLkyGrjiYmJysnJqbeiAAAA/FmtASsiIkIffPBBtfHVq1erc%2BfO9VYUAACAP6v1K8Lf/OY3mjlzpjZv3qzo6GhVVlYqJydHq1ev1rx58xqqRgAAAL9Sa8AaO3asQkJC9OKLL%2Brvf/%2B7JKlXr15auHChBg0a1CAFAgDOjb/9JwKgKao1YEnSqFGjNGrUqIaoBQAAoEmo80ajeXl5Sk9P169%2B9SsdOnRIL7/8sjZu3NgQtQEAAPilWgPW1q1bNXbsWH355ZfaunWrysrK9Omnn%2BrXv/613n///YaqEQAAwK/UGrDmzp2ryZMn68UXX1Tz5s0lSQ899JDGjx%2Bv%2BfPnN0iBAAAA/qbOM1ijR4%2BuNj5%2B/Hjt3r273ooCAADwZ7UGrObNm6uoqKja%2BIEDBxQWFlZvRQEAAPizWgPWkCFDNG/ePB09etQztnv3bs2aNUtXXXVVfdcGAADgl2oNWPfcc4%2BOHz%2Buyy%2B/XMXFxUpOTta1116rZs2aafr06Q1VIwAAgF%2Bp9T5YrVq10muvvab169dr%2B/btqqysVGRkpAYOHKigoDrv8AAAABCQ6rzRqCQNGDBAAwYMqO9aAAAAmgROQwEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMCyJhuwDh06pLS0NPXv318DBw5UZmamSktLJUn79u3TpEmTFBsbq2uuuUYffPCB13M//PBDXXvttYqJidENN9ygffv2%2BaIFAADgp5pkwDLGKC0tTcXFxXr55Zf1%2BOOPa82aNZo3b56MMUpNTVW7du20YsUK/fznP9ett96q/fv3S5L279%2Bv1NRUJScn6/XXX9dFF12kW265RcYYH3cFAAD8hdPXBdSH/Px85ebmat26dWrXrp0kKS0tTY888oiuvPJK7du3T6%2B99ppatGihrl27av369VqxYoWmTZum5cuXq3fv3po8ebIkKTMzUwkJCfroo4902WWX%2BbItAADgJ5rkGaz27dvrmWee8YSrKkVFRXK5XOrVq5datGjhGY%2BPj1dubq4kyeVyqV%2B/fp5pYWFhioqK8kwHAACoS5M8gxUeHq6BAwd6HldWVuqll17S5ZdfLrfbrYiICK/527Ztq4MHD0pSndPPRkFBgdxut9eY09mi2nLPV7NmTTIfA42O09kw%2B1rVPs2%2BjUDUUPtZQ2mSAet0c%2BbM0fbt2/X666/r%2BeefV3BwsNf04OBglZWVSZKKi4trnX42srKytGDBAq%2Bx1NRUpaWlfc8OAPhSmzYtG/T1wsPDGvT1gMagofez%2BtbkA9acOXP0wgsv6PHHH1dkZKRCQkJ05MgRr3nKysoUGhoqSQoJCakWpsrKyhQeHn7Wr5mSkqKkpCSvMaezhQ4fPv49u6gZv%2BUCDcP2vnsmzZoFKTw8TEePFquiorJBXhNoLOprP/NVcGvSASsjI0Ovvvqq5syZo6uvvlqS1KFDB%2B3atctrvsLCQs/Xdx06dFBhYWG16T179jzr142IiKj2daDbfUzl5RwwAX/U0PtuRUUlxwsEnKb2nm%2Byp0AWLFig1157TY899phGjBjhGY%2BJidG2bdtUUlLiGcvOzlZMTIxnenZ2tmdacXGxtm/f7pkOAABQlyYZsHbv3q2FCxfqpptuUnx8vNxut%2Benf//%2B6tixo9LT07Vz504tWbJEmzdv1pgxYyRJ1113nXJycrRkyRLt3LlT6enp6ty5M7doAAAAZ61JBqx//vOfqqio0FNPPaXExESvn2bNmmnhwoVyu91KTk7WX/7yFz355JPq1KmTJKlz586aP3%2B%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%2BLx1vi4BgJ/hDBYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQGrBqWlpZoxY4b69eunxMRELV261NclAQAAP8KNRmswe/Zsbd26VS%2B88IL279%2Bve%2B65R506ddKwYcN8XRoAAPADBKzTnDhxQsuXL9fTTz%2BtqKgoRUVFaefOnXr55ZcJWAAA4KzwFeFp8vLyVF5erri4OM9YfHy8XC6XKisrfVgZAADwFwSs07jdbrVp00bBwcGesXbt2qm0tFRHjhzxYWUAAMBfELBOU1xc7BWuJHkel5WV%2BaIkAADgZ7gG6zQhISHVglTV49DQ0LNaRkFBgdxut9eY09lCERERdor8P82akY8BAE2D09m0PtMIWKfp0KGDDh8%2BrPLycjmd360et9ut0NBQhYeHn9UysrKytGDBAq%2BxW2%2B9VdOmTbNaa0FBgW78wU6lpKRYD2%2BNVUFBgbKysug5AARi3/QcGD1Lgdl3oPXctOKiBT179pTT6VRubq5nLDs7W9HR0QoKOrvVlZKSopUrV3r9pKSkWK/V7XZrwYIF1c6WNWX0HDgCsW96DhyB2Heg9cwZrNOEhYVp9OjRmjlzph5%2B%2BGEVFBRo6dKlyszMPOtlREREBEQ6BwAANSNg1SA9PV0zZ87UjTfeqFatWmnatGn62c9%2B5uuyAACAnyBg1SAsLEyPPPKIHnnkEV%2BXAgAA/FCzmTNnzvR1Efj%2BWrZsqf79%2B6tly5a%2BLqXB0HPgCMS%2B6TlwBGLfgdSzwxhjfF0EAABAU8L/IgQAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsDyU6WlpZoxY4b69eunxMRELV261NclnbNDhw4pLS1N/fv318CBA5WZmanS0lJJ0kMPPaTu3bt7/bz00kue565atUpDhgxRTEyMUlNT9c0333imGWM0d%2B5cXX755erfv79mz56tysrKBu/vTFavXl2tt7S0NEnS9u3bNXbsWMXExOi6667T1q1bfVzt97d06VJddtllio6OrtZv9%2B7d1aNHD0nS1KlTq01bs2aNZznPP/%2B8Bg4cqLi4OM2YMUPFxcW%2BaqmamrZXWVmZrr32Wm3cuNHX5X1v27dvV%2B/evdWnTx9FRUVp4sSJXuu9puNPTX3n5ubqV7/6leLi4nT11Vdr%2BfLlXq8zatSoatt%2Bx44dkurejw8fPqxp06YpLi5OSUlJeuutt%2Bp5rVRXU8/1eexqjD3fe%2B%2B9Ne7fN9xwg%2Bc5/fr1qzb9%2BPHjkur%2BLNu3b58mTZqk2NhYXXPNNfrggw8artnzZeCXHnzwQTNy5EizdetW8/e//93ExcWZd955x9dlnbXKykrzy1/%2B0vzmN78xO3bsMJs2bTJDhw41f/rTn4wxxkyaNMksXrzYFBQUeH5OnDhhjDHG5XKZPn36mDfeeMN8%2BumnZsKECebmm2/2LPvZZ581gwYNMps2bTLr1683iYmJ5plnnvFJnzVZuHChmTJlildv3377rTl%2B/LhJSEgwf/rTn8yuXbtMRkaGueKKK8zx48d9XfI5e/fdd01sbKzp2bOnWbdunendu7fp0aOH%2Bfe//232799vhg4dambNmmWMMWbo0KHmrbfe8lofpaWlnuXEx8ebf/3rX8blcplrrrnG/PGPf/Rlax41ba8BAwaY3/72tyYyMtJs2LDB1yV%2BL8ePHzfR0dGmZ8%2Be5s9//rO56aabTI8ePcy9997rmef0409sbKwZM2aMV98FBQWmX79%2B5tFHHzV79uwxq1atMtHR0WbNmjXGGGPKy8tNdHS0%2Beijj7y2/cmTJ40xde/HU6ZMMTfeeKP57LPPzLJly0zv3r2Ny%2BVqsPVUUlJiUlNTq23r%2Bjx2Ncaejx496tXrJ598Ynr37m1Wr15tjDHm4MGDJjIy0nzxxRde81VWVhpjav8sq6ysNCNHjjR33nmn2bVrl1m0aJGJiYkxX331VYP1fD4IWH6o6gB46k795JNPmgkTJviwqnOza9cuExkZadxut2fs7bffNomJicYYYwYOHGjWrl1b43Pvvvtuc88993ge79%2B/33Tv3t188cUXxhhjBg0aZFasWOGZ/uabb5rBgwfXRxvfy5133mkeffTRauPLly83SUlJngNLQv86AAAMNklEQVRPZWWlGTp0qFcv/uLKK680kZGRZt%2B%2BfcYYYzZt2mS6d%2B9u0tLSzKJFi8yQIUNMaWmpKS0tNT179jT5%2Bfk1Luf66683//M//%2BN5vGnTJtOnTx/PB5Yvnb69duzYYaKiojy9%2B2vAqurriSeeMMZ89z6MjIz0rPfTjz87d%2B40V1xxhYmJifHq%2B5VXXjHDhg3zWvb9999v7rjjDmOMMXv37jU9evQwJSUlNdZR2378%2Beefe72/jDFmxowZXseF%2BrRz504zatQoM3LkyGrbur6OXY2551NNnjzZ3HXXXZ7H69atMwkJCTXOW9dn2YcffmhiY2O9fsm88cYbvY4JjRlfEfqhvLw8lZeXKy4uzjMWHx8vl8vVqL4Kq0379u31zDPPqF27dl7jRUVFKioq0qFDh/TjH/%2B4xue6XC7169fP87hjx47q1KmTXC6XDh06pAMHDui//uu/PNPj4%2BP11VdfqaCgoF56OVe7d%2B%2BusTeXy6X4%2BHg5HA5JksPhUN%2B%2BfZWbm9vAFZ6fiooKHTx4UB07dlTnzp0lSbGxsQoKCtI//vEPPf3007rzzjsVHBys/Px8ORwOXXLJJTUuZ8uWLV7bOjY2VidPnlReXl6D9XMmp2%2BvTZs26Yc//KESEhJ8XNn5yc3Nldvt9uxDDodDI0eOVFlZmfLy8qodfz766CPFx8eroqLCazlVX/ufrqioSJK0a9cudezYUSEhIdXmqWs/drlcXu%2BvqumffPLJ%2Ba%2BAs/DRRx/psssuU1ZWltd4fR67GmvPp1q/fr02bdqkO%2B64wzO2a9cuXXrppTXOX9dnmcvlUq9evdSiRQuv6f5yTCRg%2BSG32602bdooODjYM9auXTuVlpbqyJEjPqzs7IWHh2vgwIGex5WVlXrppZd0%2BeWXa/fu3XI4HFq0aJGuvPJKjRo1Sm%2B88YZn3oKCAkVERHgtr23btjp48KDcbrckeU2vCnEHDx6sz5bOijFGe/bs0QcffKCrr75aQ4YM0dy5c1VWVia3233GvvzJ0aNHJUknT570jDmdTrVu3VrSd9tm2LBhkqT8/Hy1atVK06dPV2JiosaMGaP333/fs5zS0lKvdVK1nMawTk7fXtdff70GDx6swsJCH1Z1/goLC6ut9w4dOkiSZx879fhz/fXX63e/%2B53Kysq8ltO5c2fFxsZ6Hn/99df661//qgEDBkj67heN5s2ba8qUKUpISNCECRO0efNmSapzPz7TvnLo0CEr66Au119/vWbMmKGwsDCv8fo8djXWnk%2B1ZMkS/eIXv1DHjh09Y7t371ZxcbEmTpyoxMRE3XTTTdqzZ4%2Bkuj/L/P2YSMDyQ8XFxV5vSEmex6cf5PzFnDlztH37dt1%2B%2B%2B2esxpdunTRkiVLNHbsWN1///1avXq1JKmkpKTG/svKylRSUuJ5fOo0qXGsm/3793u237x583TPPffo7bff1uzZs8%2B4XRtD3eeipm0gSc2bN1d5ebkmTJjgGcvPz1dJSYkSExP1zDPPaNCgQZo6daq2bNlyxuU0lnXSVLbX6aouZj99HzLGqKysrNbjz5mUlJRo2rRpateunVJSUiRJe/bs0bfffquxY8dqyZIl6tq1q2688UYdOHCgzv24sa77%2Bjx2Ndaeq%2Bzbt08bNmzQxIkTvcbz8/P17bffaurUqVq4cKFCQ0M1adIkFRUV1flZ1th7rovT1wXg3IWEhFR7g1U9Dg0N9UVJ52XOnDl64YUX9PjjjysyMlI/%2BclPNHjwYM8Zjx49emjv3r169dVXNXTo0DP2HxYW5rVzVn31UDVvbb95NZSLL75YGzdu1IUXXiiHw6GePXuqsrJSd999t/r3719jX/62TU9f71WqPrhHjBjhGbvllls0ceJEXXjhhZK%2B29bbtm3TsmXLdPvtt9e4nKpt7Wtneh/62/Y6XU3br6ysTM2aNVNYWJgqKirOePypyfHjx3XLLbdo7969euWVVzzbLiMjQyUlJWrVqpUkaebMmcrJydFbb72lK664wrPcmvbjxrruR48eXW/Hrsbac5X33ntPPXv2VLdu3bzGn332WZ08eVItW7aUJM2dO1eDBg3SmjVr6vwsCwkJqfatTGPquS6cwfJDHTp00OHDh1VeXu4Zc7vdCg0NVXh4uA8rO3cZGRl67rnnNGfOHF199dWSvrvmo%2BoAVaVLly6eU%2BEdOnSo9jVMYWGh2rdv7/kqo%2Bp0%2B6n/bt%2B%2Bfb31cS5at27tuW5Hkrp27arS0lK1b9%2B%2Bxr5OP0Xe2FVtu1OvrSkvL9fRo0flcDg8YUqSgoKCvB5L/7%2BtW7durZCQEK91Ul5eriNHjjSKbXmm96G/ba/TdejQodp6P3jwoCorKz372JmOP6crKirSr3/9a%2B3cuVMvvPCC17VJTqfTE64kec78HDp0qM79uLZjgC/V57GrsfZcZe3atfrpT39abTw4ONgTrqTvjgudO3f2bOfaPsv8fR8jYPmhnj17yul0el3ol52drejoaAUF%2Bc8mXbBggV577TU99thjXmc1nnjiCU2aNMlr3ry8PHXp0kWSFBMTo%2BzsbM%2B0AwcO6MCBA4qJiVGHDh3UqVMnr%2BnZ2dnq1KlTo9gp165dq8suu8zrnkKffvqpWrdu7blg1Rgj6bvrtXJychQTE%2BOrcr%2BXoKAgdezYUV999ZXnWonc3FwZY6pd7HrvvfcqPT3da6xqWwcFBSk6OtprW%2Bbm5srpdHruoeVLMTExTWJ7nS4mJkYdO3bUxx9/LOm7vv72t7%2BpefPm6tGjR63Hn1NVVlbq1ltv1ZdffqkXX3xRP/nJT7ymT5w4UQsWLPCa/7PPPlOXLl3q3I9jY2O93l9V00%2B95ssX6vPY1Vh7lr57j2zZskV9%2B/atNj5kyBCtXLnSM3bixAl9/vnn6tKlS52fZTExMdq2bZvn69Oq6X6zj/nqvy/i/Nx///1mxIgRxuVymdWrV5u%2Bffua9957z9dlnbVdu3aZnj17mscff9zr3igFBQXG5XKZXr16mWeeecZ8/vnn5uWXXza9e/c2OTk5xhhjcnJyTFRUlFm2bJnnXjJTpkzxLHvx4sUmMTHRbNiwwWzYsMEkJiaapUuX%2BqpVL8eOHTMDBw40d9xxh9m9e7f597//bRITE82SJUvMsWPHzOWXX24yMjLMzp07TUZGhklISPDL%2B2CtWrXKREVFmcjISLNx40YTHR1tIiMjzcKFC73me%2B%2B990xUVJR54403zN69e838%2BfNNnz59PP8VfdWqVaZv375m9erVxuVymREjRpiMjAxftFRNbdvLn2/TcOzYMRMVFWX69u1rnnrqKXPTTTeZ6Ohoc99993nmOdPx59S%2Bs7KyTI8ePcyaNWu89u/Dhw8bY4xZunSpiY%2BPN//4xz/M7t27zQMPPGCuuOIKc%2BzYMWNM3fvx5MmTzYQJE8ynn35qli1bZqKjoxv0nlBVTu25vo9djbFnY4zZt2%2BfiYyMNAUFBdXmzcjIMFdddZXZsGGD2bFjh0lNTTXXXnutKS8vN8bU/llWXl5urrnmGnPbbbeZHTt2mMWLF5vY2Fjug4X6deLECTN9%2BnQTGxtrEhMTzXPPPefrks7J4sWLTWRkZI0/xhizevVqM3LkSBMdHW2GDRtWLTyuWLHCDBo0yMTGxprU1FTzzTffeKaVl5ebhx9%2B2PTr189cdtllZs6cOZ57FTUGO3bsMJMmTTKxsbEmISHBzJ8/31Ofy%2BUyo0ePNtHR0WbMmDFm27ZtPq72%2B5s/f77p1auXiY6ONoMHDza9evUy//nPf6rNt2zZMvOzn/3M9O7d2/ziF78wH330kdf0xYsXmwEDBpj4%2BHiTnp5%2Bxvsm%2BcKZtpc/Byxjvuure/fuJjIy0kRFRZkbbrjBa72f6fhzat%2BTJ0%2Bucf%2BuusdRZWWleeqpp8xVV11levfubcaPH28%2B%2B%2Bwzz2vUtR8XFhaaKVOmmOjoaJOUlGTefvvtBlgz1Z2%2Brevz2NVYe87NzTWRkZGeGwSfqqSkxGRmZpqEhAQTExNjpkyZYvbv3%2B%2BZXtdn2d69e8348eNN7969zYgRI8y6devqrS/bHMb83/ltAAAAWOE/F%2BwAAAD4CQIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACz7X8eGnGW32zVjAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-686803840885803429\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">12286</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">609</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2648</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">601</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4699</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2652</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">605</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4703</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2656</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4707</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (4944)</td>\n",
       "        <td class=\"number\">4944</td>\n",
       "        <td class=\"number\">99.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-686803840885803429\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">17787</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">17788</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">17789</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">17790</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">17791</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_like_count\">like_count<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.0018167</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable288576167252669061\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 99.8%\">\n",
       "            4945\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.2%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        9\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable288576167252669061, #minifreqtable288576167252669061\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable288576167252669061\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">4945</td>\n",
       "        <td class=\"number\">99.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_view_count\">view_count<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>99</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>2.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>15.757</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>148</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.1%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram2703683907217049297\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAR5JREFUeJzt3bENwjAQQFFAjMQQ7ETNTgzBTqZH6AssRQnwXh/Jzded03g/xhg74KXD2geALTuufYBnp8vt42/u1/MCJwETBJJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCBs7hHPGR7%2BZCkmCASBQBAIhJ%2B4g8xwb%2BEdJggEgUD42xVrxqdrmZXs%2B5kgEEyQBc38CJhhUi1nP8YYax8CtsqKBUEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAuEBnKIXl5IwSvEAAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives2703683907217049297,#minihistogram2703683907217049297\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives2703683907217049297\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles2703683907217049297\"\n",
       "                                                  aria-controls=\"quantiles2703683907217049297\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram2703683907217049297\" aria-controls=\"histogram2703683907217049297\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common2703683907217049297\" aria-controls=\"common2703683907217049297\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme2703683907217049297\" aria-controls=\"extreme2703683907217049297\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles2703683907217049297\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>11</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>17</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>45</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>148</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>148</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>9</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>14.276</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.90598</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>9.9235</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>15.757</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>9.9186</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>2.5867</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>78060</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>203.79</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>38.8 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram2703683907217049297\">\n",
       "            <img 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lBS9%2BuqrkqR169Zpz549Sk9PV1VVlaZNm6amTZtq4sSJHskKAAC8j1fuwTp58qSysrKUmpqqu%2B%2B%2BW7GxsUpOTtaePXskSbm5uerUqZNCQ0Odt8DAQEnS5s2bNXDgQA0dOlQdOnTQokWLtG/fPuXl5UmSNm7cqOTkZMXGxqp79%2B6aOnWqtmzZ4rGsAADA%2B3hlwQoNDdWaNWvUrFkzl/GLFy/q4sWLOnfunNq0aXPd52ZnZys2NtZ5v3nz5oqIiFB2drbOnTungoICde3a1TkfExOjM2fOqLCw8JZkAQAA1uOVBSs4OFi9evVy3q%2BpqdHmzZvVvXt35ebmysfHR6tWrVLv3r314IMP6vXXX3c%2BtrCwUGFhYS7ba9q0qc6ePSuHwyFJLvNXS9zZs2dvZSQAAGAhXnkO1rXS0tJ07Ngx7dixQ59%2B%2Bql8fHzUtm1bPfroozp06JBmzZqlRo0aqV%2B/fiovL5e/v7/L8/39/VVZWany8nLn/W/PSVJlZWWd11NYWOgsa1fZbEG1il19%2Bfl5Vz%2B22W5%2BvVezelvmm0FWayKrNZEVN%2BL1BSstLU0bNmzQSy%2B9pPbt2%2Bvuu%2B9WfHy8GjduLEnq0KGDvvjiC23dulX9%2BvVTQEBArbJUWVmpwMBAlzIVEBDg/Lck5zlcdZGRkaH09HSXscTERCUnJ990TisICWlY720EB9f98%2BDtyGpNZLUmsuJaXl2w5s%2Bfr61btyotLU39%2B/eXJPn4%2BDjL1VVt27bVwYMHJUnh4eEqKipymS8qKlJoaKjCw8MlSQ6HQy1btnT%2BW/rmvK%2B6GjlypBISElzGbLYgFRdf%2Bh7pvpu3vYuoT34/P18FBweqtLRM1dXWvmwGWa2JrNZE1tufGW/ub4bXFqz09HRt27ZNL774ogYMGOAcX7ZsmT7%2B%2BGOtX7/eOXb8%2BHG1bdtWkmS325WZmalhw4ZJkgoKClRQUCC73a7w8HBFREQoMzPTWbAyMzMVERHxvQ7vhYWF1Xq8w3FBVVXe8wV5K5iRv7q65o75OJLVmshqTWTFtbyyYOXm5mrlypWaNGmSYmJiXM53io%2BP1%2BrVq7V27Vr169dP%2B/fv165du7Rx40ZJ0qhRozRmzBhFRUUpMjJSCxcuVJ8%2BfdSqVSvn/OLFi/XjH/9YkrRkyRJNmDDB/SEBAIDX8sqC9fbbb6u6ulqvvPKKXnnlFZe5zz77TMuWLdPy5cu1bNkytWjRQkuWLFF0dLQkKTo6WvPmzdPy5cv19ddfq0ePHpo/f77z%2BRMnTtRXX32lpKQk%2Bfn5acSIERo/frw74wEAAC/nYxiG4elF3Akcjgumb9Nm81W/xe%2BZvt1b5a2netz0c202X4WENFRx8SXL75omqzWR1ZrIevsLDf2hR17Xu86SBgAA8AIULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTub1gPfzww9q2bZsuXLjg7pcGAABwC7cXrO7du2vVqlXq2bOnpkyZov3798swDHcvAwAA4JZxe8F6%2Bumn9c4772jlypXy8/PTk08%2BqT59%2Buill17S559/7u7lAAAAmM7miRf18fFRjx491KNHD5WVlWnTpk1auXKlVq9erS5dumjcuHH62c9%2B5omlAQAA1JtHCpYkFRYW6s0339Sbb76p//znP%2BrSpYseeughnT17Vs8995wOHTqkmTNnemp5AAAAN83tBeuNN97QG2%2B8oQ8//FBNmjTR0KFDtXz5crVp08b5mObNm2vhwoUULAAA4JXcXrBmzpyp%2BPh4rVixQr1795avb%2B3TwNq2batHH33U3UsDAAAwhdsL1j//%2BU%2BFhISopKTEWa6OHDmizp07y8/PT5LUpUsXdenSxd1LAwAAMIXbf4vw4sWLGjBggH7/%2B987xyZNmqQhQ4aooKDA3csBAAAwndsL1gsvvKDWrVvrsccec4795S9/UfPmzZWamuru5QAAAJjO7QXrX//6l5599lmFhoY6x5o0aaJnnnlGBw8edPdyAAAATOf2gmWz2VRaWlprvKysjCu6AwAAS3B7werdu7cWLFigL7/80jmWl5en1NRU9erVy93LAQAAMJ3bf4tw%2BvTpeuyxx9S/f38FBwdLkkpLS9W5c2elpKS4ezkAAACmc3vBatq0qV5//XUdOHBAOTk5stlsuuuuu3T//ffLx8enzts5d%2B6cFi5cqIMHDyogIEAPPPCApkyZooCAAOXl5WnWrFnKyspSRESEZsyYoZ49ezqfe%2BDAAb3wwgvKy8uT3W7XwoUL1apVK%2Bf8%2BvXrtXbtWl28eFEDBw7UrFmzFBgYaOrHAQAAWJfbDxFKkp%2Bfn3r16qUJEyZo7Nix%2BslPfvK9ypVhGEpOTlZZWZm2bNmil156Se%2B8846WLl0qwzCUmJioZs2aaefOnRoyZIiSkpKUn58vScrPz1diYqKGDRumHTt2qEmTJpo8ebLz/K%2B//e1vSk9P17x587RhwwZlZ2crLS3tlnwcAACANbl9D5bD4dDSpUt1%2BPBhXblypdaJ7W%2B//fZ3buPkyZPKysrS%2B%2B%2B/r2bNmkmSkpOT9bvf/U69e/dWXl6etm3bpqCgILVr104ffPCBdu7cqSeffFLbt2/XvffeqwkTJkiSUlNT1aNHD3300Ufq1q2bNm7cqHHjxik%2BPl6SNHfuXE2cOFHTpk1jLxYAAKgTtxesWbNm6ejRoxo0aJB%2B%2BMMf3tQ2QkNDtWbNGme5uurixYvKzs5Wp06dFBQU5ByPiYlRVlaWJCk7O1uxsbHOucDAQHXu3FlZWVmKjY3VJ598oqSkJOd8VFSUrly5ouPHjys6Ovqm1gsAAO4sbi9YBw8e1Jo1a1xKzvcVHBzs8huHNTU12rx5s7p37y6Hw6GwsDCXxzdt2lRnz56VpP86X1paqoqKCpd5m82mxo0bO59fF4WFhXI4HC5jNltQrdetLz8/jxzhvWk2282v92pWb8t8M8hqTWS1JrLiRtxesIKCgtS0aVNTt5mWlqZjx45px44dWr9%2Bvfz9/V3m/f39VVlZKemb623daL68vNx5/0bPr4uMjAylp6e7jCUmJio5ObnO27CikJCG9d5GcPCdc5iWrNZEVmsiK67l9oI1ZMgQrVmzRvPmzXP%2Bcef6SEtL04YNG/TSSy%2Bpffv2CggIUElJictjKisr1aBBA0lSQEBArbJUWVmp4OBgBQQEOO9fO/99zr8aOXKkEhISXMZstiAVF1%2Bq8zbqwtveRdQnv5%2Bfr4KDA1VaWqbq6hoTV3X7Ias1kdWayHr7M%2BPN/c1we8EqKSnRnj179O6776pVq1a19hZt3LixztuaP3%2B%2Btm7dqrS0NPXv31%2BSFB4erhMnTrg8rqioyHl4Ljw8XEVFRbXmO3bsqMaNGysgIEBFRUVq166dJKmqqkolJSUuf9rnu4SFhdU6HOhwXFBVlfd8Qd4KZuSvrq65Yz6OZLUmsloTWXEttxcsSRo8eHC9t5Genq5t27bpxRdf1IABA5zjdrtdq1evVnl5uXOvVWZmpmJiYpzzmZmZzseXlZXp2LFjSkpKkq%2BvryIjI5WZmalu3bpJkrKysmSz2dShQ4d6rxkAANwZ3F6wUlNT672N3NxcrVy5UpMmTVJMTIzLCeVxcXFq3ry5UlJSNHnyZL3zzjs6cuSI83WHDx%2ButWvXavXq1YqPj9eKFSvUsmVLZ6EaPXq0Zs%2Berfbt2yssLExz5szRI488wiUaAABAnXlkD1ZhYaH%2B9Kc/6fPPP9eMGTN06NAhtW/fXm3btq3T899%2B%2B21VV1frlVde0SuvvOIy99lnn2nlypWaOXOmhg0bptatW2vFihWKiIiQJLVs2VIvv/yyXnjhBa1YsULR0dFasWKF80KngwYN0pkzZzR79mxVVlbqZz/7maZNm2buBwAAAFiaj3HtlT5vsVOnTumRRx5Ro0aNdO7cOb311ltKS0vTe%2B%2B9p/Xr18tut7tzOW7jcFwwfZs2m6/6LX7P9O3eKm891eOmn2uz%2BSokpKGKiy9Z/tg/Wa2JrNZE1ttfaOjNXXOzvtz%2Ba2i//e1v1bdvX%2B3du1c/%2BMEPJEkvvviiEhIStHjxYncvBwAAwHRuL1iHDx/WY4895vK3B202myZPnqxjx465ezkAAACmc3vBqqmpUU1N7V2Lly5dMuW6WAAAAJ7m9oLVs2dPvfrqqy4lq6SkRGlpaerevbu7lwMAAGA6txesZ599VkePHlXPnj1VUVGh3/zmN4qPj9fp06c1ffp0dy8HAADAdG6/TEN4eLh27dqlPXv26N///rdqamo0atQoDRkyRI0aNXL3cgAAAEznketgBQYG6uGHH/bESwMAANxybi9YY8eO/a/z3%2BdvEQIAANyO3F6wWrRo4XK/qqpKp06d0n/%2B8x%2BNGzfO3csBAAAw3W3ztwhXrFihs2fPunk1AAAA5nP7bxHeyJAhQ/TWW295ehkAAAD1dtsUrI8//pgLjQIAAEu4LU5yv3jxoj777DONHj3a3csBAAAwndsLVkREhMvfIZSkH/zgB3r00Uf14IMPuns5AAAApnN7wfrtb3/r7pcEAABwK7cXrEOHDtX5sV27dr2FKwEAALg13F6wxowZ4zxEaBiGc/zaMR8fH/373/929/IAAADqze0Fa9WqVVqwYIGmTZumuLg4%2Bfv765NPPtG8efP00EMP6YEHHnD3kgAAAEzl9ss0pKamavbs2erfv79CQkLUsGFDde/eXfPmzdPWrVvVokUL5w0AAMAbub1gFRYWXrc8NWrUSMXFxe5eDgAAgOncXrCioqL04osv6uLFi86xkpISpaWl6f7773f3cgAAAEzn9nOwnnvuOY0dO1a9e/dWmzZtZBiGvvjiC4WGhmrjxo3uXg4AAIDp3F6w2rVrp7/85S/as2ePcnNzJUm//OUvNWjQIAUGBrp7OQAAAKZze8GSpB/96Ed6%2BOGHdfr0abVq1UrSN1dzBwAAsAK3n4NlGIYWL16srl27avDgwTp79qymT5%2BumTNn6sqVK%2B5eDgAAgOncXrA2bdqkN954Q88//7z8/f0lSX379tXevXuVnp7u7uUAAACYzu0FKyMjQ7Nnz9awYcOcV29/4IEHtGDBAu3evdvdywEAADCd2wvW6dOn1bFjx1rjHTp0kMPhcPdyAAAATOf2gtWiRQt98skntcb/%2Bc9/Ok94BwAA8GZu/y3CiRMnau7cuXI4HDIMQx988IEyMjK0adMmPfvss%2B5eDgAAgOncXrCGDx%2BuqqoqvfLKKyovL9fs2bPVpEkTPfXUUxo1apS7lwMAAGA6tx8i3LNnjwYMGKB3331XBw4c0Pvvv68DBw7oscceu6ntVVZWavDgwfrwww%2BdYwsWLNA999zjctu8ebPLGvr27Su73a7ExESdP3/eOXf1MhLdu3dXXFycFi1apJqampsPDAAA7jhuL1jz5s1znszepEkTNW3a9Ka3VVFRoSlTpignJ8dlPDc3V08//bT279/vvA0fPlySdOTIEc2cOVNJSUnKyMhQaWmpUlJSnM9dt26d9uzZo/T0dC1fvly7d%2B/WunXrbnqNAADgzuP2gtWmTRv95z//qfd2Tpw4oUceeURffvllrbnc3Fx16tRJoaGhztvVP8OzefNmDRw4UEOHDlWHDh20aNEi7du3T3l5eZKkjRs3Kjk5WbGxserevbumTp2qLVu21Hu9AADgzuH2c7A6dOigqVOnas2aNWrTpo0CAgJc5lNTU%2Bu0nY8%2B%2BkjdunXT//t//09RUVHO8YsXL%2BrcuXNq06bNdZ%2BXnZ2tX//61877zZs3V0REhLKzs%2BXv76%2BCggJ17drVOR8TE6MzZ86osLBQYWFh3yMpAAC4U7m9YH3%2B%2BeeKiYmRpHpd92r06NHXHc/NzZWPj49WrVqlf/7zn2rcuLEee%2BwxPfTQQ5J03aLUtGlTnT171rmeb883a9ZMknT27FkKFgAAqBO3FKxFixYpKSlJQUFB2rRp0y19rZMnT8rHx0dt27bVo48%2BqkOHDmnWrFlq1KiR%2BvXrp/Lycuef6LnK399flZWVKi8vd97/9pz0zcn0dVVYWFirPNpsQaYXND8/tx/hrReb7ebXezWrt2W%2BGWS1JrJaE1lxI24pWOvWrdPEiRMVFBTkHJs0aZIWLFhgeukYOnSo4uPj1bhxY0nfHJL84osvtHXrVvXr108BAQG1ylJlZaUCAwNdytTVQ5dXH3v1HK66yMjIqPV3FRMTE5WcnHxBLHrPAAAZBUlEQVTTuawgJKRhvbcRHFz3z4O3I6s1kdWayIpruaVgGYZRa%2BzQoUOqqKgw/bV8fHyc5eqqtm3b6uDBg5Kk8PBwFRUVucwXFRUpNDRU4eHhkr45dNmyZUvnvyUpNDS0zmsYOXKkEhISXMZstiAVF1/6fmG%2Bg7e9i6hPfj8/XwUHB6q0tEzV1da%2BbAZZrYms1kTW258Zb%2B5vhtvPwbrVli1bpo8//ljr1693jh0/flxt27aVJNntdmVmZmrYsGGSpIKCAhUUFMhutys8PFwRERHKzMx0FqzMzExFRER8rz1tYWFhtR7vcFxQVZX3fEHeCmbkr66uuWM%2BjmS1JrJaE1lxLcsVrPj4eK1evVpr165Vv379tH//fu3atUsbN26UJI0aNUpjxoxRVFSUIiMjtXDhQvXp08f5dxBHjRqlxYsX68c//rEkacmSJZowYYLH8gAAAO/jtoLl4%2BPjlte57777tGzZMi1fvlzLli1TixYttGTJEkVHR0uSoqOjNW/ePC1fvlxff/21evToofnz5zufP3HiRH311VdKSkqSn5%2BfRowYofHjx7tl7QAAwBp8jOudIGWyDh066IEHHnC55tXu3buVkJCghg1dj43W9TpY3sbhuGD6Nm02X/Vb/J7p271V3nqqx00/12bzVUhIQxUXX7L8rmmyWhNZrYmst7/Q0B965HXdsgera9eutS5bEB0dreLiYhUXF7tjCQAAAG7jloJ1q699BQAAcDvxrt/zBwAA8AIULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTeX3Bqqys1ODBg/Xhhx86x/Ly8jR%2B/HhFRUXpgQce0P79%2B12ec%2BDAAQ0ePFh2u11jx45VXl6ey/z69evVq1cvRUdHa8aMGSorK3NLFgAAYA1eXbAqKio0ZcoU5eTkOMcMw1BiYqKaNWumnTt3asiQIUpKSlJ%2Bfr4kKT8/X4mJiRo2bJh27NihJk2aaPLkyTIMQ5L0t7/9Tenp6Zo3b542bNig7OxspaWleSQfAADwTl5bsE6cOKFHHnlEX375pcv4wYMHlZeXp3nz5qldu3Z6/PHHFRUVpZ07d0qStm/frnvvvVcTJkzQ3XffrdTUVJ05c0YfffSRJGnjxo0aN26c4uPjdd9992nu3LnauXMne7EAAECdeW3B%2Buijj9StWzdlZGS4jGdnZ6tTp04KCgpyjsXExCgrK8s5Hxsb65wLDAxU586dlZWVperqan3yyScu81FRUbpy5YqOHz9%2BixMBAACrsHl6ATdr9OjR1x13OBwKCwtzGWvatKnOnj37nfOlpaWqqKhwmbfZbGrcuLHz%2BXVRWFgoh8PhMmazBdV63fry8/Oufmyz3fx6r2b1tsw3g6zWRFZrIituxGsL1o2UlZXJ39/fZczf31%2BVlZXfOV9eXu68f6Pn10VGRobS09NdxhITE5WcnFznbVhRSEjDem8jODjQhJV4B7JaE1mtiay4luUKVkBAgEpKSlzGKisr1aBBA%2Bf8tWWpsrJSwcHBCggIcN6/dj4wsO5fUCNHjlRCQoLLmM0WpOLiS3XeRl1427uI%2BuT38/NVcHCgSkvLVF1dY%2BKqbj9ktSayWhNZb39mvLm/GZYrWOHh4Tpx4oTLWFFRkfPwXHh4uIqKimrNd%2BzYUY0bN1ZAQICKiorUrl07SVJVVZVKSkoUGhpa5zWEhYXVOhzocFxQVZX3fEHeCmbkr66uuWM%2BjmS1JrJaE1lxLe/aBVIHdrtdn376qfNwnyRlZmbKbrc75zMzM51zZWVlOnbsmOx2u3x9fRUZGekyn5WVJZvNpg4dOrgvBAAA8GqWK1hxcXFq3ry5UlJSlJOTo9WrV%2BvIkSMaMWKEJGn48OE6fPiwVq9erZycHKWkpKhly5bq1q2bpG9Onl%2B7dq327t2rI0eOaM6cOXrkkUe%2B1yFCAABwZ7NcwfLz89PKlSvlcDg0bNgwvfnmm1qxYoUiIiIkSS1bttTLL7%2BsnTt3asSIESopKdGKFSvk4%2BMjSRo0aJAef/xxzZ49WxMmTNB9992nadOmeTISAADwMj7G1UuY45ZyOC6Yvk2bzVf9Fr9n%2BnZvlbee6nHTz7XZfBUS0lDFxZcsf%2ByfrNZEVmsi6%2B0vNPSHHnldy%2B3BAgAA8DQKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAym6cXgDvHwKXve3oJ38tbT/Xw9BIAAF6KPVgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAms2zB%2Bsc//qF77rnH5ZacnCxJOnbsmB5%2B%2BGHZ7XYNHz5cR48edXnunj171LdvX9ntdiUmJur8%2BfOeiAAAALyUZQvWiRMnFB8fr/379ztvCxYs0OXLlzVp0iTFxsbqtddeU3R0tB5//HFdvnxZknTkyBHNnDlTSUlJysjIUGlpqVJSUjycBgAAeBPLFqzc3Fy1b99eoaGhzltwcLD%2B8pe/KCAgQM8884zatWunmTNnqmHDhvrrX/8qSdq8ebMGDhyooUOHqkOHDlq0aJH27dunvLw8DycCAADewtIFq02bNrXGs7OzFRMTIx8fH0mSj4%2BPunTpoqysLOd8bGys8/HNmzdXRESEsrOz3bJuAADg/SxZsAzD0Oeff679%2B/erf//%2B6tu3rxYvXqzKyko5HA6FhYW5PL5p06Y6e/asJKmwsPC/zgMAAHwXm6cXcCvk5%2BerrKxM/v7%2BWrp0qU6fPq0FCxaovLzcOf5t/v7%2BqqyslCSVl5f/1/m6KCwslMPhcBmz2YJqFbf68vOzZD%2B%2Bbdhsnvn4Xv283gmfX7JaE1mt6U7KagZLFqwWLVroww8/1I9%2B9CP5%2BPioY8eOqqmp0bRp0xQXF1erLFVWVqpBgwaSpICAgOvOBwYG1vn1MzIylJ6e7jKWmJjo/C1GeIeQkIYeff3g4Lp/zXk7sloTWa3pTspaH5YsWJLUuHFjl/vt2rVTRUWFQkNDVVRU5DJXVFTk3LsUHh5%2B3fnQ0NA6v/bIkSOVkJDgMmazBam4%2BNL3ifCdeBdxa5n9%2BaorPz9fBQcHqrS0TNXVNR5Zg7uQ1ZrIak3emtVTb5YtWbDee%2B89TZ06Ve%2B%2B%2B65zz9O///1vNW7cWDExMfr9738vwzDk4%2BMjwzB0%2BPBhPfHEE5Iku92uzMxMDRs2TJJUUFCggoIC2e32Or9%2BWFhYrcOBDscFVVV5zxck5PHPV3V1jcfX4C5ktSayWtOdlLU%2BLFmwoqOjFRAQoOeee06JiYnKy8vTokWL9Ktf/UoDBgzQkiVLtHDhQv3iF7/Qtm3bVFZWpoEDB0qSRo0apTFjxigqKkqRkZFauHCh%2BvTpo1atWnk4Fdxt4NL3Pb2E7%2BWtp3p4egkAgP9jyWNMjRo10tq1a3X%2B/HkNHz5cM2fO1MiRI/WrX/1KjRo10quvvurcS5Wdna3Vq1crKChI0jflbN68eVqxYoVGjRqlH/3oR0pNTfVwIgAA4E18DMMwPL2IO4HDccH0bdpsvuq3%2BD3Ttwvv5I17sGw2X4WENFRx8SXLH3IgqzWR9fYXGvpDj7yuJfdgAQAAeBIFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZDZPLwCAOQYufd/TS6izt57q4eklAMAtxR4sAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTcSX366ioqNDcuXP197//XQ0aNNCECRM0YcIETy8LsAxvuuq8xJXnAXx/FKzrWLRokY4ePaoNGzYoPz9f06dPV0REhAYMGODppQEAAC9AwbrG5cuXtX37dv3%2B979X586d1blzZ%2BXk5GjLli0ULAAAUCcUrGscP35cVVVVio6Odo7FxMRo1apVqqmpka8vp60BdxoOaQL4vihY13A4HAoJCZG/v79zrFmzZqqoqFBJSYmaNGnyndsoLCyUw%2BFwGbPZghQWFmbqWv38KHsAavOmQviPqb08vYR6ufpz%2BE74eXwnZTUDBesaZWVlLuVKkvN%2BZWVlnbaRkZGh9PR0l7GkpCQ9%2BeST5izy/xQWFmrcj3M0cuRI08vb7aawsFAZGRlktRiyWtOdlnXDhjVkRS3U0GsEBATUKlJX7zdo0KBO2xg5cqRee%2B01l9vIkSNNX6vD4VB6enqtvWVWRFZrIqs1kdWa7qSsZmAP1jXCw8NVXFysqqoq2WzffHgcDocaNGig4ODgOm0jLCyMdg8AwB2MPVjX6Nixo2w2m7KyspxjmZmZioyM5AR3AABQJzSGawQGBmro0KGaM2eOjhw5or179%2BoPf/iDxo4d6%2BmlAQAAL%2BE3Z86cOZ5exO2me/fuOnbsmJYsWaIPPvhATzzxhIYPH%2B7pZV1Xw4YNFRcXp4YNG3p6KbccWa2JrNZEVmu6k7LWl49hGIanFwEAAGAlHCIEAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwfJSFRUVmjFjhmJjY9WzZ0/94Q9/8PSSTHHu3DklJycrLi5OvXr1UmpqqioqKiRJeXl5Gj9%2BvKKiovTAAw9o//79Hl6teSZNmqRnn33Wef/YsWN6%2BOGHZbfbNXz4cB09etSDqzNHZWWl5s6dq65du%2BonP/mJXnzxRV29zrHV8hYUFOjxxx9Xly5dlJCQoPXr1zvnrJK1srJSgwcP1ocffugc%2B67v0QMHDmjw4MGy2%2B0aO3as8vLy3L3sm3K9rFlZWfrFL36h6Oho9e/fX9u3b3d5jpWyXnXhwgX16tVLr732msv4nj171LdvX9ntdiUmJur8%2BfPuWu5tjYLlpRYtWqSjR49qw4YNev7555Wenq6//vWvnl5WvRiGoeTkZJWVlWnLli166aWX9M4772jp0qUyDEOJiYlq1qyZdu7cqSFDhigpKUn5%2BfmeXna9/fnPf9a%2Bffuc9y9fvqxJkyYpNjZWr732mqKjo/X444/r8uXLHlxl/S1YsEAHDhzQ2rVrtWTJEv3pT39SRkaGJfM%2B9dRTCgoK0muvvaYZM2Zo6dKl%2Bsc//mGZrBUVFZoyZYpycnKcY9/1PZqfn6/ExEQNGzZMO3bsUJMmTTR58mTd7n9M5HpZHQ6Hfv3rXysuLk6vv/66kpOTNX/%2BfL377ruSrJX129LS0lRYWOgyduTIEc2cOVNJSUnKyMhQaWmpUlJS3LHc258Br3Pp0iUjMjLSOHjwoHNsxYoVxqOPPurBVdXfiRMnjPbt2xsOh8M5tnv3bqNnz57GgQMHjKioKOPSpUvOuXHjxhnLly/3xFJNU1xcbPTu3dsYPny4MX36dMMwDGP79u1GQkKCUVNTYxiGYdTU1Bj9%2BvUzdu7c6cml1ktxcbHRqVMn48MPP3SOvfrqq8azzz5rubwlJSVG%2B/btjc8%2B%2B8w5lpSUZMydO9cSWXNycowHH3zQ%2BPnPf260b9/e%2BXPou75Hly5d6vIz6vLly0Z0dLTLz7HbzY2y/vGPfzQGDBjg8thZs2YZU6ZMMQzDWlmvOnTokNGvXz%2BjR48eLl%2Bv06ZNc/7sMgzDyM/PN%2B655x7jyy%2B/dNvab1fswfJCx48fV1VVlaKjo51jMTExys7OVk1NjQdXVj%2BhoaFas2aNmjVr5jJ%2B8eJFZWdnq1OnTgoKCnKOx8TEKCsry93LNNXvfvc7DRkyRHfddZdzLDs7WzExMfLx8ZEk%2Bfj4qEuXLl6dNTMzU40aNVJcXJxzbNKkSUpNTbVc3gYNGigwMFCvvfaarly5opMnT%2Brw4cPq2LGjJbJ%2B9NFH6tatmzIyMlzGv%2Bt7NDs7W7Gxsc65wMBAde7c%2BbbOfqOsV09fuNbFixclWSur9M1hw1mzZmn27Nny9/d3mbs2a/PmzRUREaHs7Oxbvubbnc3TC8D353A4FBIS4vKF3qxZM1VUVKikpERNmjTx4OpuXnBwsHr16uW8X1NTo82bN6t79%2B5yOBwKCwtzeXzTpk119uxZdy/TNB988IH%2B9a9/affu3ZozZ45z3OFwuBQu6ZusN9pt7w3y8vLUokUL7dq1S6tWrdKVK1c0bNgw/eY3v7Fc3oCAAM2ePVvz58/Xxo0bVV1drWHDhunhhx/W22%2B/7fVZR48efd3x7/oe9cbv4RtlbdmypVq2bOm8/9VXX%2BnPf/6znnzySUnWyipJq1atUqdOndSzZ89ac4WFhV6X1V0oWF6orKys1ruIq/crKys9saRbIi0tTceOHdOOHTu0fv3662b21rwVFRV6/vnnNXv2bDVo0MBl7kafX2/NKn1zXtmpU6e0bds2paamyuFwaPbs2QoMDLRk3tzcXMXHx%2Buxxx5TTk6O5s%2Bfr/vvv9%2BSWa/6rmxWzV5eXq4nn3xSzZo108iRIyVZK%2BuJEye0bds2vfnmm9edLy8vt0xWs1GwvFBAQECtL96r96/9n7W3SktL04YNG/TSSy%2Bpffv2CggIUElJictjKisrvTZvenq67r33Xpc9dlfd6PPrrVklyWaz6eLFi1qyZIlatGgh6ZsTgbdu3arWrVtbKu8HH3ygHTt2aN%2B%2BfWrQoIEiIyN17tw5vfLKK2rVqpWlsn7bd32P3ujrOjg42G1rNNulS5c0efJkffHFF/rjH/%2BowMBASdbJahiGnnvuOSUnJ9c6deOqG2W9%2BrG4k3EOlhcKDw9XcXGxqqqqnGMOh0MNGjTwum/g65k/f77WrVuntLQ09e/fX9I3mYuKilweV1RUVGvXtLf485//rL179yo6OlrR0dHavXu3du/erejoaMtllb45vy4gIMBZriTpf/7nf1RQUGC5vEePHlXr1q1dSlOnTp2Un59vuazf9l3ZbjQfGhrqtjWa6eLFi5o4caJycnK0YcMGtWnTxjlnlaz5%2Bfn6%2BOOP9bvf/c75syo/P1/PP/%2B8fvWrX0myTtZbgYLlhTp27CibzeZywmRmZqYiIyPl6%2Bvdn9L09HRt27ZNL774ogYNGuQct9vt%2BvTTT1VeXu4cy8zMlN1u98Qy623Tpk3avXu3du3apV27dikhIUEJCQnatWuX7Ha7Pv74Y%2BevdBuGocOHD3ttVumbz19FRYU%2B//xz59jJkyfVokULy%2BUNCwvTqVOnXN7Vnzx5Ui1btrRc1m/7ru9Ru92uzMxM51xZWZmOHTvmldlramqUlJSk06dPa9OmTbr77rtd5q2SNTw8XH//%2B9%2BdP6d27dqlsLAwJScna%2BHChZJqZy0oKFBBQYHXZb0VvPv/xneowMBADR06VHPmzNGRI0e0d%2B9e/eEPf9DYsWM9vbR6yc3N1cqVK/XrX/9aMTExcjgczltcXJyaN2%2BulJQU5eTkaPXq1Tpy5IhGjBjh6WXflBYtWqh169bOW8OGDdWwYUO1bt1aAwYMUGlpqRYuXKgTJ05o4cKFKisr08CBAz297JvWtm1b9enTRykpKTp%2B/Ljee%2B89rV69WqNGjbJc3oSEBP3gBz/Qc889p88//1z/%2B7//q1WrVmnMmDGWy/pt3/U9Onz4cB0%2BfFirV69WTk6OUlJS1LJlS3Xr1s3DK//%2BduzYoQ8//FALFixQcHCw8%2BfU1UOkVslqs9lcfk61bt1aNptNTZs2VXh4uCRp1KhReuONN7R9%2B3YdP35czzzzjPr06aNWrVp5ePW3AQ9eIgL1cPnyZeOZZ54xoqKijJ49exrr1q3z9JLq7dVXXzXat29/3ZthGMYXX3xh/PKXvzTuvfdeY9CgQcb777/v4RWbZ/r06S7XksnOzjaGDh1qREZGGiNGjDA%2B/fRTD67OHKWlpca0adOMqKgo4/777zdefvll5/WgrJY3JyfHGD9%2BvNGlSxejb9%2B%2Bxrp16yyZ9drrJX3X9%2Bi7775r/OxnPzPuu%2B8%2BY9y4cV51raRvZ50wYcJ1f059%2B9pXVsl6rfj4%2BFrXbdu5c6fx05/%2B1IiKijISExON8%2BfPu2OZtz0fw7jNLy0LAADgZThECAAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYLL/D/KimCnJw76DAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common2703683907217049297\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">9</td>\n",
       "        <td class=\"number\">478</td>\n",
       "        <td class=\"number\">9.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:27%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">10</td>\n",
       "        <td class=\"number\">449</td>\n",
       "        <td class=\"number\">9.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8</td>\n",
       "        <td class=\"number\">426</td>\n",
       "        <td class=\"number\">8.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:24%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">11</td>\n",
       "        <td class=\"number\">397</td>\n",
       "        <td class=\"number\">8.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7</td>\n",
       "        <td class=\"number\">357</td>\n",
       "        <td class=\"number\">7.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">12</td>\n",
       "        <td class=\"number\">298</td>\n",
       "        <td class=\"number\">6.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6</td>\n",
       "        <td class=\"number\">228</td>\n",
       "        <td class=\"number\">4.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">13</td>\n",
       "        <td class=\"number\">224</td>\n",
       "        <td class=\"number\">4.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14</td>\n",
       "        <td class=\"number\">153</td>\n",
       "        <td class=\"number\">3.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">137</td>\n",
       "        <td class=\"number\">2.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (89)</td>\n",
       "        <td class=\"number\">1807</td>\n",
       "        <td class=\"number\">36.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme2703683907217049297\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">137</td>\n",
       "        <td class=\"number\">2.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">132</td>\n",
       "        <td class=\"number\">2.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:96%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">75</td>\n",
       "        <td class=\"number\">1.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:55%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">106</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">107</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">110</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">144</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">148</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_youtube_dislike_count\"><s>youtube_dislike_count</s><br/>\n",
       "            <small>Highly correlated</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is highly correlated with <a href=\"#pp_var_youtube_like_count\"><code>youtube_like_count</code></a> and should be ignored for analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Correlation</th>\n",
       "            <td>0.94612</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_youtube_favorite_count\"><s>youtube_favorite_count</s><br/>\n",
       "            <small>Constant</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is constant and should be ignored for analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Constant value</th>\n",
       "            <td>0</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_youtube_like_count\"><s>youtube_like_count</s><br/>\n",
       "            <small>Highly correlated</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is highly correlated with <a href=\"#pp_var_youtube_view_count\"><code>youtube_view_count</code></a> and should be ignored for analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Correlation</th>\n",
       "            <td>0.98338</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_youtube_view_count\">youtube_view_count<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2594</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>52.4%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>4025.6</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>2929460</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.1%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram549170973978421707\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAPtJREFUeJzt1bEJAlEQRVFXLGmLsCdje7IIexpzkQsbyN/gnHzgJZfZZmYuwE/X1QPgzG6rB3zbH6/DN%2B/n/Q9LwAeBJBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAjbzMzqEXBWPggEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAiED9obC49m6JdFAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives549170973978421707,#minihistogram549170973978421707\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives549170973978421707\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles549170973978421707\"\n",
       "                                                  aria-controls=\"quantiles549170973978421707\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram549170973978421707\" aria-controls=\"histogram549170973978421707\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common549170973978421707\" aria-controls=\"common549170973978421707\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme549170973978421707\" aria-controls=\"extreme549170973978421707\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles549170973978421707\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>76</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>244</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>684.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>2181.8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>13308</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>2929460</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>2929460</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>1937.8</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>43690</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>10.853</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>4063.3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>4025.6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>5336.6</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"alert\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>61.064</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>19942581</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>1908800000</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>38.8 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram549170973978421707\">\n",
       "            <img 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2BbMmRPp0gAAwDeca1/B2rx5s/Lz85Wdna1hw4Zp/vz52rp1a6TLAgAAcGfAOnXqlCorKzVkyBB7LCsrSxUVFaquro5gZQAAAC7dIgyFQpKkhIQEe6xPnz6SpKqqqrDxSKiurrZrbOP1djdel8fjrnzs9Uam3rY%2Bua1fkUCvnKFPztErZ%2BiTc27plSsDVkNDgyQpJibGHmv7vKmpKSI1Xai0tFQlJSVhY7m5ucrPz49QRV1Dz55xEX38%2BPjYiD6%2Bm9ArZ%2BiTc/TKGfrkXFfvlSsD1oVhyufz2Z9LUmxs5Bs%2Bbdo0jRkzJmzM6%2B2uM2fOGX2crp7ev8r0%2Bp3yeKIVHx%2Br2tp6tbTwP03bQ6%2BcoU/O0Stn6JNzne1VpP5x78qAlZiYKOn8VmFycrL9uST5/f6I1dUmISHhou3AUOgLNTd/s79pIr3%2BlpbWiNfgFvTKGfrkHL1yhj4519V75a6XQP6/xMREJSUlqayszB4rKytTUlJSxK%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%2B%2BecjXB4AAID7RO/bt0%2BStHnzZs2cOVOjR49Wenq6nnnmGW3fvp1XsQAAADopury8XC0tLTp06JCys7PtiYyMDH355Zc6evRoBMsDAABwn%2BiqqirV1taqsbFRCQkJ9oTX61WPHj1UVVUVwfIAAADcx9vU1KSGhgZJUkxMTNhkTEyMmpqaIlGXq1VXVysUCoWNeb3dwwKsCR6Pu/4TqNcbmXrb%2BuS2fkUCvXKGPjlHr5yhT865pVfe2NhY%2BXw%2BSbooTDU1NSk2NjYSdblaaWmpSkpKwsZyc3OVn58foYq6hp494yL6%2BPHxnMtO0Stn6JNz9MoZ%2BuRcV%2B%2BV1%2B/3q0ePHvL5fKqpqVG/fv0kSc3NzTp79qz8fn%2BES3SfadOmacyYMWFjXm93nTlzzujjdPX0/lWm1%2B%2BUxxOt%2BPhY1dbWq6WlNSI1uAW9coY%2BOUevnKFPznW2V5H6x703EAgoOjpaaWlpKisr09ChQyVJ5eXl8nq9Sk1NjUhhbpaQkHDRdmAo9IWam7/Z3zSRXn9LS2vEa3ALeuUMfXKOXjlDn5zr6r3ytgWqhx56SIsXL1ZKSooSEhK0ZMkSTZ06lS1CAACATvJGRUVJku69915VVFRo8eLFampq0rhx47RgwYIIlwcAAOA%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%2B019erVS48//rgsy5IkvfXWWyopKdHSpUu1adMmBYNBPf/88xFZHwAAcCfXBqzjx49r6tSp%2BvTTT8PG33//fZ04cUJLly5Vv379NG/ePGVkZGj79u2SpG3btmnw4MGaPXu2brvtNq1YsUIVFRXat2%2BfJGnz5s2aOXOmRo8erfT0dD3zzDPavn07r2IBAADHXBuw9u3bp6FDh6q0tDRsPBgMauDAgerevbs9lpWVpfLycns%2BOzvbnouNjdWgQYNUXl6ulpYWHTp0KGw%2BIyNDX375pY4ePXqVVwQAAK4X3kgX8HU99NBDlxwPhUJKSEgIG%2Bvdu7eqqqo6nK%2BtrVVjY2PYvNfrVY8ePez7O1FdXa1QKBQ25vV2v%2Bhxr5TH46587PVGpt62PrmtX5FAr5yhT87RK2fok3Nu6ZVrA9bl1NfXKyYmJmwsJiZGTU1NHc43NDTYty93fydKS0tVUlISNpabm6v8/HzHx7ge9ewZF9HHj4%2BPjejjuwm9coY%2BOUevnKFPznX1Xl13Acvn8%2Bns2bNhY01NTerWrZs9/9Ww1NTUpPj4ePl8Pvv2V%2BdjY50/kdOmTdOYMWPCxrze7jpz5pzjYzjR1dP7V5lev1MeT7Ti42NVW1uvlpbWiNTgFvTKGfrkHL1yhj4519leReof99ddwEpMTNTx48fDxmpqauztucTERNXU1Fw0P2DAAPXo0UM%2Bn081NTXq16%2BfJKm5uVlnz56V3%2B93XENCQsJF24Gh0Bdqbv5mf9NEev0tLa0Rr8Et6JUz9Mk5euUMfXKuq/fKXS%2BBOBAIBPThhx/a232SVFZWpkAgYM%2BXlZXZc/X19Tpy5IgCgYCio6OVlpYWNl9eXi6v16vU1NRrtwgAAOBq113AysnJUd%2B%2BfVVQUKBjx45p3bp1OnjwoKZMmSJJmjx5sg4cOKB169bp2LFjKigoUHJysoYOHSrp/MXzGzZs0G9/%2B1sdPHhQS5Ys0dSpUzu1RQgAAL7ZrruA5fF49NOf/lShUEiTJk3Sr3/9a61du1ZJSUmSpOTkZK1Zs0bbt2/XlClTdPbsWa1du1ZRUVGSpHvvvVfz5s3T4sWLNXv2bKWnp2vBggWRXBIAAHCZKKvtLcxxVYVCXxg/ptcbrTuL/2D8uFfLb344PCKP6/VGq2fPOJ05c65L79d3BfTKGfrkHL1yhj4519le%2Bf3fugZVXey6ewULAAAg0ghYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwvobGxkYtWrRI2dnZGjFihF5%2B%2BeVIlwQAALoQb6QLcKOioiIdPnxYmzZt0smTJ/XUU08pKSlJd999d6RLAwAAXQABq5Pq6uq0bds2rV%2B/XoMGDdKgQYN07Ngxbd26lYAFAAAksUXYaUePHlVzc7MyMzPtsaysLAWDQbW2tkawMgAA0FXwClYnhUIh9ezZUzExMfZYnz591NjYqLNnz6pXr16qrq5WKBQKu5/X210JCQlGa/F43JWPvd7I1NvWJ7f1KxLolTP0yTl65Qx9cs4tvSJgdVJ9fX1YuJJk325qapIklZaWqqSkJOxr8vLy9MQTTxitpbq6WjNvOqZp06YZD2/Xk%2Brqam3a9BJ9coBeOUOfnKNXztAn59zSq64d/7ogn89nB6k2bbe7desmSZo2bZp27NgR9jFt2jTjtYRCIZWUlFz0ahnC0Sfn6JUz9Mk5euUMfXLOLb3iFaxOSkxM1JkzZ9Tc3Cyv93z7QqGQunXrpvj4eElSQkJCl07VAADg6uIVrE4aMGCAvF6vysvL7bGysjKlpaUpOpp2AgAAAlanxcbGauLEiVqyZIkOHjyo3/72t3r55Zc1Y8aMSJcGAAC6CM%2BSJUuWRLoItxk2bJiOHDmilStX6r333tNjjz2myZMnR6SWuLg45eTkKC4uLiKP7xb0yTl65Qx9co5eOUOfnHNDr6Isy7IiXQQAAMD1hC1CAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyA5VKNjY1atGiRsrOzNWLECL388suRLsm1jhw5ov79%2B4d9DB061J5/9913NWHCBAUCAc2YMUMnTpwIu/8rr7yikSNHKjMzU4sWLVJ9ff21XoIRZ86c0dChQ5Wenq7%2B/fsrNTX1orW2udyaOzovT5w4oVmzZikjI0P33HOP9uzZEzbfUa%2BvlU8%2B%2BUTp6elhY2fOnNETTzyhzMxMjRkzRr/61a/aPcbOnTs1duxYBQIB5ebm6vTp01ez5K/lSp%2BvSLIsS8XFxRo2bJhycnJUVFSk1tbWSJcF/I0FV1q6dKl13333WYcPH7b%2B8z//08rMzLR%2B85vfRLos12ltbbUGDhxo5eTkWAcOHLBeeOEFKzU11aqoqLAsy7IqKiqsjIwMa8OGDdaf//xn68knn7QmTJhgtba2WpZlWW%2B%2B%2BaaVlZVl/dd//ZcVDAate%2B65x3rmmWciuaSv7eGHH7YGDBhgrVmzxtqyZYs1aNAgKz093V5rm/bW3N552draat13333Wj370I%2Bv48ePWCy%2B8YAUCAce9vlZOnjxp3XXXXVZKSkrY%2BLx586yZM2daf/rTn6xXX33VGjx4sBUMBi95jGAwaKWnp1u/%2BMUvrI8%2B%2Bsh6%2BOGHrblz516L8jvlSp6vSNuwYYN1xx13WPv377fee%2B89a8SIEdZLL70U6bIAGwHLhc6dO2elpaVZ77//vj22du1a6%2BGHH45gVe707rvvWhkZGdb9999vj82cOdNavXq1ZVmWtWrVqrC%2B1tXVWZmZmXbvH3roIftrLcuy9u/fb6Wnp1t1dXXXaAVmfPLJJ1ZKSoo1evRoe%2BzHP/5x2FrbXG7NHZ2Xbb0%2Bd%2B6cPd%2BZXl8Lb7/9tjVs2DDrvvvuCwtYbf05ceKEPbZo0SLrqaeeuuRxFixYEDZ38uRJq3///tann3569YrvpCt9viLtjjvusLZv327f/uUvfxl2/gKRxhahCx09elTNzc3KzMy0x7KyshQMBnmJvJOCwaBuvfVWpaam2mNZWVn275oMBoPKzs6252JjYzVo0CCVl5erpaVFhw4dCpvPyMjQl19%2BqaNHj167RRgQDAZ1ww036P7777fHhgwZIr/fH/Z7N9tb80cffdTueRkMBjVw4EB17949bN5Jr9u8/fbbF23n5ufn2/NHjhzRAw88oEAgoMmTJ%2Bvw4cNh62xv286yLK1evVoNDQ3661//Kkn291MwGFTfvn2VnJwcVvv//M//XLafF66lb9%2B%2BSkpKUjAYvOTXO2F6%2B7SjnyMdPV%2BRdOrUKVVWVmrIkCH2WFZWlioqKlRdXX1VHrOjc89N/vjHP%2BrOO%2B%2B0LwV48sknjf3dcT1dvnLkyBENHjxY6enpGjRokB555JGwS0A6WisBy4VCoZB69uypmJgYe6xPnz5qbGzU2bNnI1iZ%2B4RCId14442qqqrSlClTNHLkSP385z/XJ598Ys8nJCSE3ad3796qqqpSbW2tGhsbw%2Ba9Xq969Oihqqqqa7qOKxUKhZSUlBS2lt69e%2BvTTz8NW0t7az516lS752V7vWyrob15STp%2B/LhGjx6tPXv22B/PPfecJKmurk5z585Vdna2duzYoczMTM2bN08zwWFJAAAKUklEQVR1dXWSpIMHD6qwsFB5eXkqLS1VbW2tCgoK7GNv3LhRtbW1Wr9%2BvZ544gl7rL3aTp06dcl%2BVldXd7iWzjh58qRyc3M1adIkvfbaa%2BrVq5cef/xxWVfwizg6%2Bjni5PmIlFAoJElh9fXp00eSrlp97Z17bnLy5En94z/%2Bo2pqavSzn/1MvXr10jvvvGMsCBUVFenw4cPatGmTnn76aZWUlOjNN980cuxrqa6uTg8%2B%2BKBaW1u1YMECDR8%2BXB988IGWLl1qf017a7Usi4DlRvX19WE/FCXZt5uamiJRkmvV19dr3759%2Br//%2Bz8VFBToJz/5id3LlpaWy/a6qalJDQ0N9u1LzbtJfX29Wltbw9YSExMjy7LC1tLRmts7L9vrZVsNHfXy448/VkpKivx%2Bv/0RHx8vSdq1a5d8Pp8WLlyofv36qbCwUHFxcfYPvC1btmj8%2BPGaOHGiUlNTVVRUpN27d9uvBG3evFn5%2BfnKzs7WwIEDJUlbt251XNuFGhoajJ4X27Zt0%2BDBgzV79mzddtttWrFihSoqKrRv376vdTyp458jnV3ztXSp8/Bq/wxs79xzk23btqlXr176l3/5F%2BXk5Oh3v/udmpqa9O///u9XfOy6ujpt27ZNhYWFGjRokO688049%2Buij9veRm%2BzatUt%2Bv1%2BPPfaYHnnkEb344otqbW3Vrl27VF9f3%2BFa33//fQKWG/l8vot%2BiLTd7tatWyRKci2fz6e7775bW7ZsUVZWlrKzszVlyhSdOHFCwWDwsr2OjY2Vz%2Bezb19q3k0utZampiZ5PJ6wtbS35kv95XvheXm5Xrads%2B31us3HH3%2BsW2%2B99ZJrCAaDysrKUlRUlCQpKipKt99%2B%2B2W3IC/ctrvUlpMke8upo9q/yslaOsPJ9mlndfRzpLNrvpYuFabaPr9a33vtnXtuEgwGw8712NhYJSUlGdlevZ4uXykvL1coFLL7FBUVpfvuu09NTU06evSooy12ApYLJSYm6syZM2pubrbHQqGQunXr5sp/UUVSYmKiTp8%2BHfaXRmtrq7xer06dOqXExETV1NSE3aempkZ%2Bv189evSQz%2BcLm29ubtbZs2fl9/uv2RpMSExMVFVVVdhaampqlJycHLaW9tbc0Xl5uV62bfO012vp/Evuf/nLX7Rnzx7dddddGjt2rIqLi%2B2/WDva0mpv2%2B5SW05tqqqqOqztqzr79R25Gtt1V/p8RVJiYqKkv20VXvj51fje6%2Bjcc5O26wYvfB7b3pLkSrdXr6fLV2pqai66HKLtvGv7mdHRFjsBy4UGDBggr9cb9q/XsrIypaWlKTqap7QzbrvtNu3bt0%2B7d%2B%2B2x/bv36/m5mb93d/9nQKBgMrKyuy5%2Bvp6HTlyRIFAQNHR0UpLSwubLy8vl9frDbto3g3aLlS/cC0ffPCBqqurFQgE7LH21jxw4MB2z8tAIKAPP/zQ3t5pm287fnu9ls5fO9K2bbVq1So99dRTev3111VUVGR/fXtbWu1t211u61M6/8pIRkaGKioqwv4CKisrU0ZGxiX7%2BdW1VFZWqrKyMqyXnXE1tus6%2BjnS0fMVSYmJiUpKSgrrcVlZ2UXXEZrS0bnnJldze/V6unyl7WL2y1024WSL3XvtyoUpsbGxmjhxopYsWaLly5erurpaL7/8slasWBHp0lxn1KhR8vl8ysvL04YNG3TgwAEdPnxYQ4cOVf/%2B/RUXF6cNGzZo3bp1Gj16tNauXavk5GT7jUgfeughLV68WCkpKUpISNCSJUs0depU120R3nLLLRoxYoT27NmjkpIS9ezZUzt27JDP5wt701Wp/TW3d17m5OSob9%2B%2BKigo0OOPP6533nlHBw8etOcnT57cbq9vvvlm7d27V9/%2B9rcVFRWlAQMG2BegFhQUXNEW5IU/GNu2QdvExsba/VmwYIEKCwt16NAh7dy5U1u2bLlkP6dPn65HHnlEGRkZSktL07JlyzRq1CjdcsstX%2BfpuWztV/KK9ZU%2BX5E2ffp0FRcX66abbpIkrVy5UrNnz74qj9XRuefxeK7K414NPp9P9fX1Yee6qe3V6%2BnylY4um2hpaelwi52A5VIFBQVasmSJZs6cqRtvvFFPPPGExo0bF%2BmyXMfj8WjLli364Q9/qB/84Afyer361re%2BpTVr1kiSkpOTtWbNGi1fvlxr165VZmam1q5da1/nc%2B%2B996qiokKLFy9WU1OTxo0bpwULFkRySV9bUVGR/v7v/15r1qxRVFSUoqOj9cYbb9hrbdPemts7Lz0ej37605%2BqsLBQkyZN0ne/%2B12tXbtWSUlJkjrutXR%2Bi/JC/fr1U2Njoz7//PMr2oK8cMvpwrdikP625VRUVKTCwkJNnTpVfr9fy5cvv%2Bjd3ttkZmZq6dKlWr16tT7//HMNHz5czz77bAfPwOVdrvYBAwZ87WNKV/Z8RdqcOXP02WefKS8vTx6PR1OmTNGsWbOu2uO1d%2B716tXrqj2uabfffrv%2B%2BMc/hp3rbW8fcqXbqxduO3u95%2BOFWy9fSUxMtC%2BH6Nevn6TzW4Otra3y%2B/2yLKvdtSYmJvJO7gDc4b//%2B7%2BtnJycsDdx/fWvf23l5ORYlmVZ27Zts8aNG2e/83tra6s1duxY67XXXrMs6/ybfy5atMi%2B71ff/HPUqFHWL3/5S3v%2BF7/4hTVq1Kirvi4nVq1aZc2cOdO%2B3fYmrO%2B9917kivoG6ejcc5NVq1ZZqamp9rleV1dnpaSkGDnX6%2BrqrLS0NGv//v32WElJifX973//io99rbX9PCkpKbEs6/zPk9TUVCstLc2qq6vrcK3vvvuu5VmyZMmSCIZEAHCkd%2B/eKi0t1Z/%2B9CfddtttOnjwoJ599ln94Ac/UFZWlr7zne/opZdeUlVVlZKSkvRv//ZvOnr0qJYuXaobbrhBffr00YoVK%2BT3%2B%2BXxeOxtzu9///uSzr%2B8/8ILL2jQoEGqqKjQM888o1mzZoX9L6FISU5OVnFxsTwej%2BLj47VixQq1tLRo/vz5F73CCPM6OvfcJDk5WZs2bdLvfvc79ezZU3l5eZKkuXPnXvG5fsMNN6iyslI///nPNXjwYB06dEjPP/%2B8fvSjH9mvArnFd77zHf3kJz/RoUOHZFmW1q9fr8rKSv3DP/yD7rrrrg7XmpSUxCtYANzjz3/%2BszVr1iwrIyPDGj58uLVmzZqw31UYDAatiRMnWmlpadaUKVOsDz/8MOz%2B27dvt%2B644w4rIyPDys3NtU6fPm3PNTc3W8uXL7eys7OtoUOHWs8///w1/z2I7fn9739vjRs3zkpPT7dmzpzZpX7tzjdBR%2Beem7zzzjvW6NGjrZSUFCslJcV68sknja2lrq7OWrhwoZWRkWGNGDHC2rhxo5HjRkIwGLT69%2B9vpaSkWIMGDbJmzJhhNTQ02PMdrTXKsq7grYABAABwEf5PPwAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY9v8ApfbpryxSFoMAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common549170973978421707\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">118</td>\n",
       "        <td class=\"number\">14</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">71</td>\n",
       "        <td class=\"number\">13</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">112</td>\n",
       "        <td class=\"number\">12</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">59</td>\n",
       "        <td class=\"number\">12</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">76</td>\n",
       "        <td class=\"number\">12</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">149</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">174</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">170</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">137</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">131</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (2584)</td>\n",
       "        <td class=\"number\">4836</td>\n",
       "        <td class=\"number\">97.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme549170973978421707\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">10</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">15</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">198333</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">253924</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">363216</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">395780</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2929460</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Correlations</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsEAAAJ1CAYAAAA46i0XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3X1czXf/B/DXOZ1KN7qRyiwqLtUkOTRhudDMPYu5J8SwLPf3ldhi7ra5fnS5udrFMLvcX7ZrbofNzUYMCRPphthUJKTbU%2Bf3R%2Bu7jkpxzqc66/V8PDxOfW/e3/f5OJ3e593n%2B/3K1Gq1GkREREREtYi8uhMgIiIiIqpqLIKJiIiIqNZhEUxEREREtQ6LYCIiIiKqdVgEExEREVGtwyKYiIiIiGodFsFEREREVOuwCCYiIiKiWodFMBERERHVOorqToCISJ/dvXsXb7/9drnrDQ0NYW5uDicnJ3Tu3BkjR46Eubl5FWZIRERlkfG2yUREr65kEezi4lKqwM3Pz0d6ejru3bsHAGjYsCG%2B/PJLODo6VnmuRET0JxbBRERaKFkEb9myBd7e3mVuFxUVhUmTJiEzMxNKpRLbt2%2BvyjSJiOg5nBNMRFQFvL29MWPGDADApUuXcPXq1WrOiIiodmMRTERURd555x3p68uXL1djJkRExBPjiIiqSN26daWvnz17prHu/Pnz2Lp1Ky5evIiMjAxYWFigVatW8Pf3R/v27cuM9%2BTJE2zfvh0nTpzArVu3kJmZCRMTEzRu3BhdunTBqFGjYGlpqbGPq6srAOCnn37CsmXLcOzYMcjlcri7u2Pjxo1QKBS4fPkyNm3ahAsXLiA9PR2mpqZwdnZG165dMXz48DJP7MvJycH27dtx4MAB3Lp1C/n5%2BbC3t0eHDh0wduxYODk5aWwfFRWFUaNGwdPTE9u2bcPWrVuxb98%2B3L59G4aGhmjevDlGjRqFrl27vspQExFViEUwEVEVuX37tvR1gwYNpK8//fRTREZGAgAsLS3h4uKC1NRUHDt2DMeOHcP48eMxa9YsjVhJSUkYM2YMfv/9dygUCjRu3Bivv/467t27h2vXruHatWvYv38/9uzZAzMzs1K5TJ48GZcuXYKLiwvS09Nha2sLhUKBI0eOYPr06VCpVLC2toarqyuePXuGmJgYXL58Gd9%2B%2By22b9%2BuUQjfv38fAQEBSEhIAAA4OTnBzMwM8fHx2LFjB/bt24dly5ahV69epfLIz8/H%2BPHjcebMGVhbW6Np06ZITExEVFQUoqKisGjRIgwbNky7gSciKouaiIheWXJystrFxUXt4uKiPnv27Au3nTNnjtrFxUXt7u6uTktLU6vVavV//vMftYuLi9rLy0v9zTffSNsWFhaq9%2B/fr27VqpXaxcVFvXPnTo1YI0eOVLu4uKgHDx6sTklJ0djvv//9r9rNzU3t4uKi/uqrrzT2K861RYsW6nPnzqnVarW6oKBA/ejRI3VBQYH6rbfeUru4uKgjIyPVKpVK2u/q1avqdu3aqV1cXNQbNmyQlqtUKvW7776rdnFxUXfv3l19/fp1ad3Tp0/VISEh0nOOjo6W1p09e1bKpVWrVupvv/1WWvfkyRP16NGj1S4uLuq2bduq8/PzXziuRESvgnOCiYgEysnJwa%2B//oqFCxdi3759AIAxY8agfv36yMvLw5o1awAAn3zyCfr16yftJ5PJ0KtXL8yePRsAsGbNGqhUKgDAgwcPEBcXBwAIDw%2BHnZ2dxn5%2Bfn5o27YtAODGjRtl5tWzZ0%2B8%2BeabAAC5XA4rKyukp6cjLS0NADB48GAYGBhI27u7u2P69Ono2rUrrKyspOWHDh3C9evXYWxsjMjISLi5uUnrzM3NsXjxYnTs2BH5%2BflYtWpVmblMmTIFffv2lb6vW7eu9LwzMjKQmJhYzugSEb06TocgItKRUaNGVbjNoEGDMHXqVABFV4l48OABzMzMyr3hRr9%2B/RAeHo6UlBT8%2BuuvaNmyJerXr4%2BzZ88iJycHderUKbVPQUGBNF0hJyenzLht2rQptcza2hqWlpZ4/PgxZs2ahcDAQHh6ekIuL%2BqXDB48GIMHD9bY5/jx4wAAX19fNGrUqMxjBQQE4NSpUzh37hyePn2qMTcaALp06VJqn6ZNm0pfP3nypMy4RETaYBFMRKQjz98sQyaTwdjYGFZWVnB1dUXXrl3xt7/9TVpf3M3Nz8/HiBEjyo1rYGCAwsJCJCQkoGXLltLyOnXq4N69e7hy5Qru3LmD5ORkxMfH4/r168jKygIAFBYWlhnT1ta2zOPMmjULCxYswIkTJ3DixAlYWlrC29sbb731Fjp37qwxlxmA1KV1d3cvN//idQUFBbh9%2BzZatGihsd7e3r7UPiWL%2B4KCgnJjExG9KhbBREQ6EhoaWu7NMsry9OlTAEBeXh4uXrxY4fYlO6IJCQlYuHAhzp07p7GNubk5vLy8kJqaitjY2HJjldVBBoq6vY6Ojti0aRN%2B/vlnPH78GEeOHMGRI0cgk8nQqVMnfPTRR1IxnJmZCQClurvP51Ts%2BatiAEW3ln4RNe/pREQCsAgmIqomJiYmAIo6pXv37q30fg8fPsTIkSPx8OFDNGzYEIMHD0bz5s3RpEkTODg4QCaTYebMmS8sgl/E29sb3t7eyMnJwS%2B//ILz58/j1KlTuHbtGn788UdMnDgR%2B/btg0wmk648UVzQl6Vk8V7WlSqIiKoDi2Aiomri7OwMoOhyZyqVCgpF6bdktVqNqKgoNGjQAA0bNoSRkRH27NmDhw8fwsrKCnv27EG9evVK7ZeSkvLS%2BeTl5SE5ORmZmZnw9PREnTp14OPjAx8fH0yfPh379%2B/HjBkzEBsbixs3bsDNzQ1NmjTBr7/%2BimvXrpUb98qVKwCKpoc0btz4pfMiIhKBV4cgIqomb775JurWrYtnz56V2wn%2B3//%2Bh9GjR6Nnz564f/8%2BAODu3bsAgIYNG5ZZAN%2B6dQvR0dEAXm4%2B7cmTJ9GrVy9MmDABeXl5pdZ36NBB%2Bro4bvFJbcePH0dycnKZcbds2QIAaNWqFSwsLCqdDxGRSCyCiYiqiampKSZMmAAAWLJkCfbs2aNxItvRo0excOFCAEWXNCvuojZp0gQAEBsbi8OHD0vbq9VqnDx5Eu%2B//z7y8/MBANnZ2ZXO5%2B9//zusra2RkZGBuXPnIiMjQ1qXmZmJ5cuXAwBee%2B01NGvWDADQo0cPuLq6Ijc3F%2BPHj9eYgpGZmYkFCxbg9OnTUCgUpW74QURUnTgdgoioGo0fPx7JycnYuXMngoODsXLlSjg4OCAlJQWpqakAii5ntmTJEmmfgQMH4uuvv8bt27cxZcoUvP7667C2tsbvv/%2BOhw8fwtDQEG3btsW5c%2BdealqEkZER/u///g/jxo3DgQMHcOzYMTRu3BhyuRzJycnIysqCiYkJli1bBiMjIwCAQqHA2rVrMX78eCQkJODdd9/VuGNc8WXcPvroI3h5eel28IiItMAimIioGslkMoSHh6N79%2B7Yvn07oqOjpZtPtGrVCn369MGQIUOkohMoutrC7t27ERkZiR9%2B%2BAF3797FgwcP0KBBA3Tu3BmjR4%2BGqakpunbtitjYWPz2229o2LBhpfLx9vbGrl27sGnTJly4cAFJSUlQKBRo0KABfHx8MHbs2FKxHBwcsGfPHvznP//BoUOHEB8fj/v37%2BO1115Dx44dMWLECDg5Oely2IiItCZT89ozRERERFTLcE4wEREREdU6LIKJiIiIqNZhEUxEREREOpOXl4c%2BffogKiqq3G1%2B/fVXDBo0CJ6ennjvvfdw9epVjfXfffcdunbtCk9PT3z44YdIT0/XeZ4sgomIiIhIJ3JzczFjxgzExcWVu01WVhYmTJgALy8v7N27F0qlEhMnTkRWVhYAICYmBiEhIQgKCsKOHTvw5MkTzJ8/X%2Be5sggmIiIiIq3dunULgwcPxp07d1643YEDB2BsbIw5c%2BagadOmCAkJgZmZGQ4dOgQA%2BOqrr9CzZ0/4%2BfnBzc0NK1aswIkTJ8q9Ic%2BrYhFMRERERFo7d%2B4cvL29sWPHjhdud/nyZbRp0wYymQxA0aUiW7duLd3p8vLlyxrXFX/ttdfQsGFDXL58Waf58jrBRERERITU1FSkpaVpLLO1tYWdnV2l9h8%2BfHiltktLS8Pf/vY3jWU2NjbSFIrU1NRSx7SxsZFuHa8rLIJJP/zxaVHnnJ2BuDigWTMgMVHIITKfirkUt0wGmJoCWVmAvl3tuypyNzYWExcAFApApRIXPyPjqZC4crkM9eqZIT39GQoLxQz8xx/XFRLXxgYICwM%2B/hh4%2BFDIIQAAn34qJq6REZCXJyZ2MbnAv%2B2Kfs2XuFu4zokee5HvNS8k4PfijtWrERERobEsKCgIkydP1ulxsrOzNW4ABBTdsTLvj/%2BonJycF67XFRbBVLtZWQEGBkWPekYm%2B/OfPhbB%2Bpo7IO4zmWhyuQwymQxyuUxYESyKiUlRkWdiUt2ZvDx9f70D%2Bvua/yuMfVUaMmQIfH19NZbZ2trq/DjGxsalCtq8vDzUqVPnhetNdPwGwCKYiIiISN8IaP3b2dlVeuqDNuzt7fHgwQONZQ8ePJCOXd56XRfkPDGOiIiIiKqMp6cnLl26BPUf7Xm1Wo2LFy/C09NTWn/hwgVp%2B99//x2///67tF5XWAQTERER6Ru5XPf/BEpLS0NOTg4AoEePHnjy5AmWLFmCW7duYcmSJcjOzkbPnj0BAMOGDcM333yDXbt2ITY2FnPmzEHnzp3RqFEjnebEIpiIiIhI3%2BhZEezj44MDBw4AAMzNzbFhwwZcuHABAwYMwOXLl/Gvf/0LpqamAAClUomPP/4Y//znPzFs2DBYWlpi6dKlOs%2BJc4KJiIiISKdu3Ljxwu9btmyJ//73v%2BXuP2DAAAwYMEBIbsVYBBMRERHpG8Gd29qAI0hEREREtQ47wURERET6hp1grbEIJiIiItI3LIK1xhEkIiIiolqHnWAiIiIifcNOsNY4gkRERERU67ATTERERKRv2AnWGotgIiIiIn3DIlhrHEEiIiIiqnXYCSYiIiLSN%2BwEa40jSFq7e/cuXF1dcffu3VLr9u7dC19f32rIioiIiKh87AST1l577TWcPn0a9erVq%2B5UiIiIagd2grXGIpi0ZmBgAFtb2%2BpOg4iIqPZgEaw1jiBpreR0iJSUFLz//vto1aoV%2Bvfvjzt37lR3ekRERESlsBNMOjV16lSYmppi165diIuLQ0hICKytrV8qRmpqKtLS0jSW2To7w87KSpepFnFz03wUQNSHdZnsz0d9awjoc%2B5VQaEQMygGBnKNRxEcHMTEtbfXfBSl%2BLUpIqaI2H8VosZG9Nir1WLiVgrfPLXGIph0Jjs7G5cuXcIPP/yAhg0bolmzZrh69SoOHTr0UnF27NiBiIgIjWVBU6di8tSpukxX09dfCwttKixyERMTwQcQSJ9zNzQUF9va2kxccAAWFuIGfu5cYaEBAGPGiI0vksjXTFXQ5/xF5Z6bKyYuVQ0WwaQzJ06cgJWVFRo2bCgt8/DweOkieMiQIaWuKGHbty%2BwebNO8tTg5lZUAA8fDsTG6j4%2BgKzTF4XElcmKisjs7GruRryCqshd5C9sQ0MgP19c/MzMZ0LiGhjIYWFhgidPslFQUCjkGP/6l5gC3t6%2BqAD%2B8ksgJUXIIQAA06frPqZM9udrRuTPqshOs%2BjXvKhxqaqxrxbsBGuNRTDplPq5dxnDV6hE7OzsYGdnp7kwMVGbtCoWGwtcuiQkdKGYWkN6/1OrxR1DFH3OvSqoVGIHpaCgUNgxyrhSok6lpIg9hshCSa3W3yJYNNEFquixrxYsgrXGESSd6dKlCx4/fozbt29Ly65fv16NGRERERGVjUUw6YyxsTHat2%2BP4OBgxMbG4ujRo/jqq6%2BqOy0iIqK/Hrlc9/9qmdr3jEmoVatWwdraGkOHDsXnn38Of3//6k6JiIiIqBTOCSatOTg44MaNG9L3pa7sEBRU1SkRERH9tdXCzq2usQgmIiIi0jcsgrXGESQiIiKiWoedYCIiIiJ9w06w1jiCRERERFTrsBNMREREpG/YCdYai2AiIiIifcMiWGscQSIiIiKqddgJJiIiItI37ARrjUUwERERkb5hEaw1jiARERER1TrsBBMRERHpG3aCtcYRJCIiIqJah51gIiIiIn3DTrDWWAQTERER6RsWwVrjCBIRERFRrcNOMBEREZG%2BYSdYazK1Wq2u7iSIKpKZKSauXA6YmgJZWUBhoZhjmNeViQmsVAIXLwKtWwOXLok5hkLQ52SlEjh3DmjbVlzuon5BtGoFREUB3t5AdLSQQ6TdfSAkrkIhh7W1GR49egaVSswL3jYrXUhcGBkBr70G/P47kJcn5hgA0KCB7mPKZEX55%2BUBIn/lihoXuRwwMwOePRP3RinqvUYmA%2BrUAXJyxI29iYmYuBXx9tZ9zKgo3ceswdgJJiIiItI37ARrjUUwERERkb6pgUVwbm4uPvroIxw5cgR16tTB2LFjMXbs2FLb%2Bfv749y5c6WWDxgwAEuXLsXjx4/Rtm1bjXVWVlaI0nGnmkUwEREREWltxYoVuHr1KjZv3ozffvsNc%2BfORcOGDdGjRw%2BN7dasWYP8/Hzp%2B8uXL2PatGkYPnw4AODWrVuwsrLCd999J20jF1D0swgmIiIi0jc1rBOclZWFXbt2ITIyEu7u7nB3d0dcXBy2bdtWqgi2srKSvi4oKMCqVavw/vvvw8PDAwCQkJAAZ2dn2NraCs25Zo0gEREREemd2NhYqFQqKJVKaVmbNm1w%2BfJlFL7ghMq9e/fi8ePHGD9%2BvLTs1q1bcHJyEpkuAHaCiYiIiPSPgE5wamoq0tLSNJbZ2trCzs6uwn3T0tJgbW0NIyMjaVn9%2BvWRm5uLjIwM1KtXr9Q%2BarUaX3zxBUaNGgUzMzNpeXx8PFQqFQYOHIiUlBR4eXlh/vz5lcrjZbAIJiIiItI3AorgHTt2ICIiQmNZUFAQJk%2BeXOG%2B2dnZGgUwAOn7vHIu3xcVFYX79%2B9j8ODBGssTEhJQr149zJ8/H2q1GqtWrcIHH3yAXbt2wcDA4GWe0guxCCYiIiIiDBkyBL6%2BvhrLKjsv19jYuFSxW/x9nTp1ytzn8OHD%2BPvf/64xRxgA9u/fD5lMJu23evVq%2BPj44PLly2jdunWl8qkMFsFERERE%2BkZAJ9jOzu6VpxzY29vj0aNHUKlUUPxx85O0tDTUqVMHFhYWZe5z6tQpBAUFlVpu8twNSGxsbGBlZYWUlJRXyq08PDGOiIiIiLTyxhtvQKFQILrEnTQvXLgADw%2BPMi9vlp6ejuTkZLRp00ZjeWZmJt58802cPXtWWpaSkoJHjx6hSZMmOs2ZRTARERGRvpHLdf9PCyYmJvDz88OiRYsQExODo0ePYuPGjRg1ahSAoq5wTk6OtH1cXByMjY3h4OCgEcfc3Bxt2rTB0qVLERMTg2vXrmH69Ono2LEjXF1dtcrxeSyCiYiIiPRNDSuCAWD%2B/Plwd3fH6NGj8dFHH2Hy5Mno1q0bAMDHxwcHDhyQtn348CEsLCwgk8lKxVm%2BfDmaN2%2BOCRMmwN/fH6%2B//jo%2B/fRTrfN7HucEExEREZHWTExMsHz5cixfvrzUuhs3bmh836tXL/Tq1avMOJaWlli6dKmQHEtiEUxERESkb2rYHeP0EUeQiIiIiGoddoKJiIiI9A07wVpjEUxERESkb1gEa40jSERERES1DjvBRERERPqGnWCt/WVGMC8vDzt37qz09q6uroiKihKYUdn8/f2xZs2aKj9udUhOTsaJEyeqOw0iIiKiUv4yRfD%2B/fuxfv366k6jQmvWrMHYsWOrO40qERwcjJiYmOpOg4iI6K%2BnBt4sQ9/8ZaZDqNXq6k6hUqysrKo7BSIiItJ3tbBo1bUqH8HQ0FB88MEHGsvCw8Mxe/Zs3L9/H1OnTkXbtm3h7e2NxYsXIy8vDwCwd%2B9e%2BPr6auxXPLUgKioK8%2BfPx7179%2BDq6oq7d%2B%2BWmnZw9%2B5daV2x8%2BfPo1u3bvD09MTUqVPx%2BPFjad3Nmzfh7%2B%2BPli1bonv37ti2bVulnt/Jkyfh6emJ7Oxsadnp06fRunVr5OTklMpr%2B/bt8PX1hVKphL%2B/v3RHlcWLF2PKlCnSduvWrUOLFi2Qm5sLAEhMTISHhweysrIqzOn27dsYN24clEolOnfujC1btkjr4uPjMW7cOLRu3RodO3ZEREQECgsLARR1rf39/TVi%2Bfr6Yu/evQCKxn/dunUYN26cNE6nTp0CAMybNw/nzp1DREREqRhERERE1a3KO8G9e/fGhAkTkJmZCXNzcxQWFuLw4cNYtGgRRo8eDUdHR2zduhXp6elYsGABgKLC%2BUWUSiWCg4OxceNG7N69G/Xq1atULtu2bcOyZctgY2OD4OBgLF26FMuWLUNOTg7Gjx%2BP/v37Izw8HAkJCViwYAHMzMzg5%2Bf3wpgdOnSAiYkJTp48ie7duwMAjhw5Al9fX9SpU0dj2%2BPHjyMiIgLh4eFwdnbGvn37MGrUKBw5cgQdO3bEvHnzoFarIZPJcP78eahUKly5cgVeXl74%2Beef0aZNG5iamr4wn9zcXIwdOxbu7u7YuXMnkpOTMXPmTDRq1Aienp4YPnw4fH19sWvXLiQmJiI0NBTm5uYYM2ZMpcZw/fr1WLhwIRYuXIjPPvsMCxYswPHjxxESEoKkpCQolUpMnDixUrGKpaamIi0tTWOZmZkt7OzsXipOZRTfslwmE/ihWqkUE9fNTfNRBIWgtwhXV81HEcq4H71OVEHuCoWYF6OBgVzjUQgjIzFxi1%2BLol6TxUS8bkq%2B0Ygk6k2sOK7IzqOosRE99tX5V2h2grVW5UWwt7c3LC0tcfz4cfTr1w%2B//PIL8vPzAQApKSnYuXMnLC0tAQBhYWEIDAzE9OnTXxjTyMgIdevWhYGBAWxtbSudS1BQEDp16gSgqNAOCAhAaGgoDh48CBsbG0ybNg0A4OTkhHv37mHLli0VFsEKhQLdunXDkSNH0L17dxQUFODo0aNYvHhxqW2/%2BOILTJw4EV26dAEATJs2DSdPnsS3336LgQMH4unTp4iLi0OTJk0QHR0NHx8fXLx4USqCO3bsWOFzPH36NNLT0/HJJ5/A3NwczZo1Q2hoKORyOb777juYmJggPDwcCoUCTZs2RVpaGv75z39Wugju1KkTBgwYAAAIDAzEu%2B%2B%2Bi7S0NNjb28PQ0BCmpqYvPQVkx44diIiI0Fj24YdBmDJl8kvFeRkmJsJCAxcvCgwO4OuvxcYXaevW6s7g1ZX4i4quWQuLXMTCQuQL3kxgbAAv8R5f4xgaio0v6gNIMaFvlIIZG4uJW%2BKvvqR/qrwIlsvl6NmzJw4dOoR%2B/frh4MGDeOedd5CQkAAnJyepAAaA1q1bQ6VS4c6dO0Jy8fDwkL5u3ry5dKyEhATExsZCWaKDV1BQAAMDg0rF7d27NyZNmoS8vDxcunQJ%2Bfn58PHxKbVdfHw8Vq5cic8//1xalpubi6SkJJiYmKBNmzY4d%2B4ccnJy8Prrr6NTp0746aefUFBQgHPnzmHq1KkV5pKYmAhnZ2eYm5tLy9577z0AwMKFC%2BHu7g5Fic6KUqlEWloanjx5Uqnn6uTkJH1dfAyVSlWpfcszZMiQUlNfzMxsUYmZHy9NJit6X8/OFveB3tSntZjAbm5FBfDw4UBsrJhjiOwEb90K%2BPsDf0wB0jmRneAtW4BRo4Tl/ujQcSFxDQzksLAwwZMn2SgoKBRyDOucyr13vDSFoqgATksDtHyPeSEbG93HlMmKCuD8fLGdwz8aSjonl//5Rlko5nWDSv5%2BfWkyWVEBnJtbvV1bEdgJ1lq1nBjXp08f%2BPv7IzMzE99//z1WrlyJmzdvltquoKBAepSV8QvtZYqt4lgllSxqi0%2BsMzQ0hEqlQvv27REWFlbp%2BCW9%2BeabMDU1xc8//4xTp06ha9euMCrjE3pBQQGCg4PRvn17jeXFxeRbb72Fc%2BfOITc3F61bt0abNm0QERGBK1euwNTUFC4uLhXmonhBEWNcxifj4vnAlR1zwzI6G9qepGhnZ1dq6kNmppj33uL3ELVa3Hs7Ll0SFPgPsbHijiH6T883bojLXfQviBs3gOhoIaFVKlEvxiIFBYXijvHHeRzCqFRijyGyUFKrxcYX9iZWIr6oY4j%2BeRU99tWBRbDWqmUEPT09YW9vj8jISKjVarRt2xbOzs5ISkpCRkaGtF10dDQUCgUaN24MQ0NDPHv2TFqnVqs1TnJ7vmAzMjLS2D45OblUHiUL75iYGBgaGsLBwQHOzs5ITEyEg4MDHB0d4ejoiOjoaGyt5J9u5XI5evTogR9//BHHjh1D7969y9zO2dkZ9%2B/fl47h6OiI9evXI/qPX6wdO3bE%2BfPnceHCBXh5ecHNzQ0qlQpbtmwps7NcFicnJ9y%2BfVvjRL3ly5dj8eLFcHZ2xrVr16TpKABw6dIl1KtXD1ZWVqXG/NmzZ0hPT6/UcYmIiIhqsmr7GNGrVy9s2rQJPXr0gIGBAd566y00atQIc%2BbMwY0bN3D27FmEh4ejT58%2BsLCwQIsWLZCRkYGtW7ciOTkZS5cu1biag4mJCR4/foykpCSoVCq0aNECBw8eRExMDGJiYrB69epSOaxatQpnzpxBdHQ0Fi9ejKFDh8LExAT9%2BvVDTk4OwsLCEB8fjxMnTmDJkiWweYk/k/Xu3RvffPMNcnNz0a5duzK3CQgIwObNm7Fv3z7cuXMHK1euxMGDB9G0aVMAgJubG%2BRyOU6ePIk2bdpALpdDqVTiwIEDlZoPDAA%2BPj6oX7%2B%2B9FyOHTuG7du3w8fHB3379kVeXp607ujRo1izZg2GDRsGmUwGDw8PxMbG4uDBg0hMTERYWBjkL/HJ09TUFElJSXj48GGl9yEiIqJK4HWCtVatRXBubi569eoFoGhqwtq1awEAgwcPxowZM/D222/j448/BlDU0Zw7dy7WrVsHPz8/qNVq6eoLANCuXTs4Ojqib9%2B%2BuH79OgICAtC8eXOMHDkSM2fOxKRJk0rlEBAQgJCQEAQEBECpVGLWrFkAiqYjREZGIikpCX5%2BfggNDcWIESNe6ioHrVq1grVhObccAAAgAElEQVS1Nbp161bulIRevXph%2BvTpWL16Nfr06YMzZ85g3bp10jxbmUyGDh06oH79%2BmjYsCEAwMvLC3K5HB06dKhUHgqFAmvXrkVqair69%2B%2BPJUuWYM6cOejcuTPMzc3xxRdf4M6dO/Dz80N4eDhGjx6NoKAgAED79u0xZswYhIWFYejQoWjWrBk8PT0rPQaDBg3CqVOn8P7771d6HyIiIqKqIFNX010mfvrpJyxYsADHjh0rc%2B4pUUmZmWLiyuWAqSmQlSVuqpt5XUGvb6Wy6MoTrVvr35xgpRI4dw5o21b/5gS3agVERQHe3sLmBKfdfSAkrkIhh7W1GR49eiZsTrBtlqApU0ZGwGuvAb//LnZOcIMGuo8pkxXln5cndl6qqHGRywEzM%2BDZM3FvlKLea2QyoE4dICdH3NhX11UzRFyDX5%2Bv2PMKqvzEuNTUVFy4cAEbNmzAwIEDWQATERERvaxaOH1B16q8CH769CmCg4PRqlUrBAQEVPXhtbZkyRLs3r273PUTJ04sdUc8kby9vaW76pVl//790lQKIiIiIipS5UVw06ZNcUn0JaMECgwMxMiRI8tdX/I6x1Vh9%2B7d0mXNyiLiLmtERERUzdgJ1lq1XCdYn9WrV6/St2WuCo0aNaruFIiIiKiqsQjWGkeQiIiIiGoddoKJiIiI9A07wVrjCBIRERFRrcNOMBEREZG%2BYSdYayyCiYiIiPQNi2CtcQSJiIiIqNZhJ5iIiIhI37ATrDWOIBERERHVOuwEExEREekbdoK1xiKYiIiISN%2BwCNYaR5CIiIiIah12golEUwj6MSuOq1CIO4ZKJTauSiXuGObmYuIaGv75aGQk5hj6rLBQbNzCQnHHKHkcXZLJ/oytVus%2BfjGR41IcX/QxqPLYCdYaR5CIiIiIah12gomIiIj0DTvBWmMRTERERKRvWARrjSNIRERERLUOO8FERERE%2BoadYK1xBImIiIio1mEnmIiIiEjfsBOsNY4gERERkb6Ry3X/T0u5ubkIDg6Gl5cXfHx8sHHjxnK3DQwMhKurq8a/H374QVr/5ZdfomPHjlAqlQgODkZ2drbW%2BT2PnWAiIiIi0tqKFStw9epVbN68Gb/99hvmzp2Lhg0bokePHqW2jY%2BPx8qVK9G%2BfXtpmaWlJQDg8OHDiIiIwMqVK2FjY4P58%2Bdj5cqVCAsL02m%2BLIKJiIiI9E0Nmw6RlZWFXbt2ITIyEu7u7nB3d0dcXBy2bdtWqgjOy8vD3bt34eHhAVtb21KxtmzZgtGjR6NLly4AgI8%2B%2Bgjjxo3D7NmzYWJiorOca9YIEhEREZHeiY2NhUqlglKplJa1adMGly9fRuFzt9tOSEiATCZDo0aNSsUpKCjAlStX4OXlJS1r1aoV8vPzERsbq9Oc2QkmIiIi0jcCOsGpqalIS0vTWGZraws7O7sK901LS4O1tTWMjIykZfXr10dubi4yMjJQr149aXlCQgLMzc0xZ84cnDt3Dg0aNMDkyZPRqVMnPHnyBLm5uRrHVCgUsLKywv3793XwLP/EIpiIiIhI3wgognfs2IGIiAiNZUFBQZg8eXKF%2B2ZnZ2sUwACk7/Py8jSWJyQkICcnBz4%2BPpgwYQK%2B//57BAYGYseOHahfv77GviVjPR9HWyyCiYiIiAhDhgyBr6%2BvxrKy5uyWxdjYuFSRWvx9nTp1NJZPmjQJ/v7%2B0olwbm5uuHbtGnbu3Inp06dr7Fsyli7nAwMsgomIiIj0j4BOsJ2dXaWmPpTF3t4ejx49gkqlgkJRVF6mpaWhTp06sLCw0NhWLpdLBXCxJk2a4NatW7CysoKxsTEePHiApk2bAgBUKhUyMjIqXZBXFk%2BMIyIiIiKtvPHGG1AoFIiOjpaWXbhwAR4eHpA/V7DPmzcP8%2BfP11gWGxuLJk2aQC6Xw8PDAxcuXJDWRUdHQ6FQwM3NTac5swgmIiIi0jc17GYZJiYm8PPzw6JFixATE4OjR49i48aNGDVqFICirnBOTg4AwNfXF//73/%2Bwb98%2B3L59GxEREbhw4QJGjhwJABg%2BfDj%2B/e9/4%2BjRo4iJicGiRYswePBgTocgIiIiqvVq2HWCAWD%2B/PlYtGgRRo8eDXNzc0yePBndunUDAPj4%2BGDp0qUYMGAAunXrhoULF2LdunX47bff0KxZM3zxxRdwcHAAAPTu3Rv37t1DWFgY8vLy0K1bN8yePVvn%2BbIIJiIiIiKtmZiYYPny5Vi%2BfHmpdTdu3ND4ftCgQRg0aFC5sSZMmIAJEyboPMeSat7HCAHy8vKwc%2BfOSm/v6uqKqKgoIbn4%2B/tjzZo1AIrmxMybNw8AsGbNGvj7%2Bws5ZnVJTk7GiRMnqjsNIiKiv54aNh1CH9WKZ7x//36sX7%2B%2ButMoJSQkBCEhIdWdhjDBwcGIiYmp7jSIiIiISqkV0yHUanV1p1CmunXrVncKREREpI9qYedW12rUCIaGhuKDDz7QWBYeHo7Zs2fj/v37mDp1Ktq2bQtvb28sXrxYupDy3r17S13cuXjaQVRUFObPn4979%2B7B1dUVd%2B/e1ZiSAAB3796V1hU7f/48unXrBk9PT0ydOhWPHz%2BW1t28eRP%2B/v5o2bIlunfvjm3btr3S8y05HaKk3NxcDBs2DGPHjpWe4/bt2%2BHr6wulUgl/f/9Sc2te5Pbt2xg3bhyUSiU6d%2B6MLVu2SOvi4%2BMxbtw4tG7dGh07dkRERIR0j%2B%2Bypmj4%2Bvpi7969AIrGeN26dRg3bpw0FqdOnZKe27lz5xAREfGXm%2BZBRERU7TgdQms1qhPcu3dvTJgwAZmZmTA3N0dhYSEOHz4snWno6OiIrVu3Ij09HQsWLABQVDi/iFKpRHBwMDZu3Ijdu3dr3Lv6RbZt24Zly5bBxsYGwcHBWLp0KZYtW4acnByMHz8e/fv3R3h4OBISErBgwQKYmZnBz89P6zEoLCzEjBkzUFhYiIiICBgZGeH48eOIiIhAeHg4nJ2dsW/fPowaNQpHjhwpdbHp5%2BXm5mLs2LFwd3fHzp07kZycjJkzZ6JRo0bw9PTE8OHD4evri127diExMRGhoaEwNzfHmDFjKpXv%2BvXrsXDhQixcuBCfffYZFixYgOPHjyMkJARJSUlQKpWYOHHiS41BWfcuNzOr3L3LX5ZM9uejsJ9/pVJMXFdXzUcRVCoxcYuv9ajjaz5qMDUVE9fFRfNRAIVCzIvRwECu8SjEc7c61RlDQ81HUYrfFETEFBG7JFFvYsVxRRZJosZG9NjX0L80U%2BXUqCLY29sblpaWOH78OPr164dffvkF%2Bfn5AICUlBTs3LlTKvrCwsIQGBgo3V6vPEZGRqhbty4MDAxe6k4jQUFB6NSpE4CiQjsgIAChoaE4ePAgbGxsMG3aNACAk5MT7t27hy1btuikCA4PD8ft27fx1VdfwfSPX%2BJffPEFJk6ciC5dugAApk2bhpMnT%2BLbb7%2BtsMt6%2BvRppKen45NPPoG5uTmaNWuG0NBQyOVyfPfddzAxMUF4eDgUCgWaNm2KtLQ0/POf/6x0EdypUycMGDAAABAYGIh3330XaWlpsLe3h6GhIUxNTWFlZfVSY1DWvcs//DAIU6ZUfO/yV6XjSw9qOndOYHAAW7eKjS/S119XdwavbuNGYaGthUUuYmEh8AVvbSYuNgAI%2BDBcZYyNxcZ/7ta0Omcm%2BP9WJFFjn50tJm5l1MLOra7VqCJYLpejZ8%2BeOHToEPr164eDBw/inXfeQUJCApycnDS6nq1bt4ZKpcKdO3eE5OLh4SF93bx5c%2BlYCQkJiI2NhbJEd6%2BgoAAGBgZaH/PSpUs4f/48PD09NZ5rfHw8Vq5cic8//1xalpubi6SkpApjJiYmwtnZGebm5tKy9957DwCwcOFCuLu7S7c3BIo652lpaXjy5EmlcnZycpK%2BLj6GSsvuYVn3Ljczs0VWllZhyySTFRXA2dniPtCbdm4rJrCra1EB7O8PvMT0mJcishP89dfA8OFAbKyYY4jsBG/cCIwdC9y8KeQQj/53WEhcAwM5LCxM8ORJNgoKCoUcwzorQ0hcGBoWFcCpqcAfzREhbGx0H1MmKyrCcnPFdg5FjYtcXlQAP3sGFIp53Qjr8FfV2JNeqlFFMAD06dMH/v7%2ByMzMxPfff4%2BVK1fiZhm/aAoKCqRHWRl/5niZQqw4Vkkli9riE%2BsMDQ2hUqnQvn17hIWFVTp%2BZZmZmSEiIgITJ07E7t27pevnFRQUIDg4GO3bt9fYvmRhW56SBe7zjMv4ZFw8H7iy42pYxhuXticilnXv8sxMMe%2B9xR%2Bk1Wpx7%2B24dElQ4D/cuCHuGKKK4GKxseJyr8TPh1Zu3gQuXxYSWqUS9WIsUlBQKO4Yf5zHIEx%2BvthjiCyU1Gqx8YW9iZWIL%2BoYogtU0WNfHdgJ1lqNG0FPT0/Y29sjMjISarUabdu2hbOzM5KSkpCR8WeHofg%2B0o0bN4ahoSGePXsmrVOr1RonuT1fzBkZGWlsn5ycXCqPkoV3TEwMDA0N4eDgAGdnZyQmJsLBwQGOjo5wdHREdHQ0turgT9IuLi548803ERgYiM8%2B%2B0x6vs7Ozrh//750PEdHR6xfv17j/tzlcXJywu3bt5Fd4k82y5cvx%2BLFi%2BHs7Ixr165JU06Aom50vXr1YGVlVWpcnz17hvT0dK2fJxEREWmJJ8ZprUY%2B4169emHTpk3o0aMHDAwM8NZbb6FRo0aYM2cObty4gbNnzyI8PBx9%2BvSBhYUFWrRogYyMDGzduhXJyclYunSpxtUcTExM8PjxYyQlJUGlUqFFixY4ePAgYmJiEBMTg9WrV5fKYdWqVThz5gyio6OxePFiDB06FCYmJujXrx9ycnIQFhaG%2BPh4nDhxAkuWLIGNDv%2BENnr0aFhaWkrTHwICArB582bs27cPd%2B7cwcqVK3Hw4EE0bdq0wlg%2BPj6oX7%2B%2BlO%2BxY8ewfft2%2BPj4oG/fvsjLy5PWHT16FGvWrMGwYcMgk8ng4eGB2NhYHDx4EImJiQgLC4P8JX5ITE1NkZSUhIcPH77yWBARERGJUGOL4NzcXPTq1QtA0dSEtWvXAgAGDx6MGTNm4O2338bHH38MoKjbOXfuXKxbtw5%2Bfn5Qq9Xo3r27FK9du3ZwdHRE3759cf36dQQEBKB58%2BYYOXIkZs6ciUmTJpXKISAgACEhIQgICIBSqcSsWbMAFE1BiIyMRFJSEvz8/BAaGooRI0a89BUQXsTIyAjz58/Hrl27cOXKFfTq1QvTp0/H6tWr0adPH5w5cwbr1q3TmI9bHoVCgbVr1yI1NRX9%2B/fHkiVLMGfOHHTu3Bnm5ub44osvcOfOHfj5%2BSE8PByjR49GUFAQAKB9%2B/YYM2YMwsLCMHToUDRr1gyenp6Vfh6DBg3CqVOn8P7777/qUBAREVFZ2AnWmkxdA%2B8k8dNPP2HBggU4duxYmfNSqfbJzBQTVy4vOn8qK0vcVDdza0EnfCiVRVeeaNtW/%2BYEK5XAxYtA69b6NyfY0xM4fRrw8RE2Jzgt4TchcRUKOaytzfDo0TNhc4JtMx8IiQsjI%2BD114F798TOCW7QQPcxZbKiKzfk5IidlypqXORyoG5d4OlTcW%2BUoi6tVxVjL/TyQi%2Bwbp3uYwYG6j5mDVajToxLTU3FhQsXsGHDBgwcOJAFMBEREREJUaOK4KdPnyI4OBitWrVCQEBAdafz0pYsWYLdu3eXu37ixIml7oinLW9vb%2BmucmXZv38/GjZsqNNjEhERUTWrhdMXdK1GFcFNmzbFJdGXkxIoMDAQI0eOLHd9RXd3exW7d%2B%2BWLmtWFhF3WSMiIiLSdzWqCNZ39erVq/RtmXWlUaNGVXo8IiIiqgHYCdYai2AiIiIifcMiWGscQSIiIiKqddgJJiIiItI37ARrjSNIRERERLUOO8FERERE%2BoadYK2xCCYiIiLSNyyCtcYRJCIiIqJah51gIiIiIn3DTrDWOIJEREREVOuwE0xERESkb9gJ1hqLYCIiIiJ9wyJYayyCSS8YG4uNb2goMLioNyqZ7M9HUccwNxcT19T0z0dRx8jMFBM3K%2BvPR1HH0GcWFmLiGhgUPZqZAXXqiDkGIDa26DcykbkD4n5Wq4LosSe9xCKYiIiISN%2BwE6w1jiARERER1TrsBBMRERHpG3aCtcYimIiIiEjfsAjWGkeQiIiIiGoddoKJiIiI9A07wVrjCBIRERFRrcNOMBEREZG%2BYSdYayyCiYiIiPQNi2CtcQSJiIiIqNZhJ5iIiIhI37ATrDWOIBERERHVOuwEExEREekbdoK1xhEkIiIi0jdyue7/aSk3NxfBwcHw8vKCj48PNm7cWO62P/74I959910olUr07dsXx44d01jv5eUFV1dXjX/Pnj3TOseS2AkmIiIiIq2tWLECV69exebNm/Hbb79h7ty5aNiwIXr06KGxXWxsLIKCgjBnzhx06tQJp0%2BfxtSpU7F79264ubkhJSUFT58%2BxdGjR1GnTh1pP1NTU53myyKYiIiISN/UsOkQWVlZ2LVrFyIjI%2BHu7g53d3fExcVh27ZtpYrg7777Du3atcOoUaMAAI6Ojjh%2B/DgOHjwINzc3xMfHw9bWFo0aNRKaM4tgIiIiItJKbGwsVCoVlEqltKxNmzZYv349CgsLIS9RtPfv3x/5%2BfmlYjx9%2BhQAcOvWLTg7OwvPmUUwERERkb4R0AlOTU1FWlqaxjJbW1vY2dlVuG9aWhqsra1hZGQkLatfvz5yc3ORkZGBevXqScubNm2qsW9cXBzOnDmDoUOHAgDi4%2BORnZ0Nf39/JCYm4o033kBwcLDOC2MWwURERET6RkARvGPHDkRERGgsCwoKwuTJkyvcNzs7W6MABiB9n5eXV%2B5%2B6enpmDx5Mlq3bo23334bAJCQkIDHjx9jxowZMDc3R2RkJMaMGYP9%2B/fD3Nz8ZZ9WuWrWhJLn5OXlYefOnZXe3tXVFVFRUQIz%2BtPdu3fh6uqKu3fvvtSxS27n6%2BuLvXv3AgD8/f2xZs0acQlXgzNnziA%2BPr660yAiIqJKGDJkCPbu3avxb8iQIZXa19jYuFSxW/x9yZPbSnrw4AFGjx4NtVqN1atXS1Mm/v3vf2Pfvn3o0KEDWrZsiU8//RS5ubn44YcftHh2pdXoTvD%2B/fuxfv16DB48uLpTqdDp06dhaWn5Uvvs3r1b52c61iRjxozBli1bSv3Zg4iIiLQkoBNsZ2dXqakPZbG3t8ejR4%2BgUqmgUBSVl2lpaahTpw4sLCxKbZ%2BSkiKdGLdlyxaN6RJGRkYaXWVjY2M4ODggJSXllXIrT43uBKvV6upOodJsbW1L/RmgIvXq1Sv30xERERGRvnjjjTegUCgQHR0tLbtw4QI8PDw0TooDiq4k8f7770Mul%2BOrr76Cvb29tE6tVqNr167SX8qLt799%2BzaaNGmi05x1UgSHhobigw8%2B0FgWHh6O2bNn4/79%2B5g6dSratm0Lb29vLF68WGqP7927F76%2Bvhr7FU8LiIqKwvz583Hv3j1p2sHzUwaen5IAAOfPn0e3bt3g6emJqVOn4vHjx9K6mzdvwt/fHy1btkT37t2xbdu2Sj/H/Px8hIeHw8vLC3//%2B99x4sQJjfUlpzmcOXMG7777Ljw8PPD2229j%2B/btZcYsOR2ipDt37qBDhw5YvXo1gKI/JyxevBje3t7w9vbGrFmzkJGRUencT548if79%2B8PT0xP9%2BvXDmTNnpHU//PAD%2Bvfvj5YtW6JXr144cuSItK6i8XZ1dcU333yDPn36oEWLFhg%2BfDiSk5Ol5wYAo0aN%2BstN8yAiIqp2NexmGSYmJvDz88OiRYsQExODo0ePYuPGjVK3Ny0tDTk5OQCADRs24M6dO1i%2BfLm0Li0tDU%2BfPoVMJkPnzp2lWjAuLg5z5sxBgwYN0KlTJ%2B3G7Dk6mQ7Ru3dvTJgwAZmZmTA3N0dhYSEOHz6MRYsWYfTo0XB0dMTWrVuRnp6OBQsWACgqnF9EqVQiODgYGzduxO7duzXa5C%2Bybds2LFu2DDY2NggODsbSpUuxbNky5OTkYPz48ejfvz/Cw8ORkJCABQsWwMzMDH5%2BfhXGXbNmDX744QesW7cOCoUC8%2BbNK3O7goICTJs2DWPGjEHfvn1x8eJFzJ07F15eXvjb3/5W4XHS09Mxbtw49OzZE1OmTAEAfP7557h69SoiIyNhbGyMVatWYerUqdi8eXOF8eLi4hAYGIgPP/xQKnInTZqEI0eO4NatW5g8eTJmzZqFTp064ccff8T06dOxY8cOtGjRosLYxeMSHh4OGxsbTJ06Ff/4xz/w2WefYffu3Wjfvj3WrFmDt956q1KxipV1dqq1tS1sbV/tTzTVrlUrMXFdXTUfRTA0FBPXxUXzUYSsLDFx3dw0HwVQKMT8kc7AQK7xKITMQExcAwPNR6LaroZdJxgA5s%2BfL9V%2B5ubmmDx5Mrp16wYA8PHxwdKlSzFgwAAcPnwYOTk5GDRokMb%2B/fv3x7JlyzB79mwoFArMnDkTmZmZaNeuHf71r3/BQMc//zopgr29vWFpaYnjx4%2BjX79%2B%2BOWXX6Trv6WkpGDnzp3SfNmwsDAEBgZi%2BvTpL4xpZGSEunXrwsDAALa2tpXOJSgoSPqkEBoaioCAAISGhuLgwYOwsbHBtGnTAABOTk64d%2B8etmzZUmERrFarsWvXLsydOxdvvvkmACA4OBgTJkwote3Tp0%2BRkZGB%2BvXrw8HBAQ4ODrCzs6vUc8jKysKECRPQsmVL6UNCdnY2vvrqK%2BzZsweufxQ7K1asgLe3N27cuCEtK8/u3bvRunVrTJo0CQAwYcIEZGVl4cmTJ9i2bRu6d%2B%2BOMWPGAACcnZ0RExODjRs34vPPP68wXwAICAhA%2B/btAQDDhg2TuuvFH1osLS1hZmZWqVjFyjo79cMPgzBlSsVnp74qUbUeAED0yZpbtoiNL9ILbqlZ4339tbDQ1sIiF7GwMBF8BIHq1q3uDF6dTFbdGWhHn/MXlbseTdusCiYmJli%2BfLnU4S3pxo0b0teHDh16YRxjY2PMmzev3IajruikCJbL5ejZsycOHTqEfv364eDBg3jnnXeQkJAAJycnjRPGWrduDZVKhTt37uji0KV4eHhIXzdv3lw6VkJCAmJjYzUu4lxQUFCpTxWPHj1Ceno63njjjTKPU5KVlRWGDRuG0NBQrF27Fl26dMF7771XqZPmtm7dCpVKBW9vb8j%2B%2BIFNTk5Gfn6%2BdO28YoWFhUhKSqqwCE5MTIS7u7vGsuIPAvHx8aXiKpVK7Nmzp8Jcizk6Okpfm5ubl3nx65c1ZMiQUtNkrK1toYPQZTI0hLDYAGDo4y0msKtrUQE8ahRQ4s1Fp0R2gjduBMaOBW7eFHMMkZ3gr78Ghg8HYmOFHOLRsVNC4hoYyGFhYYInT7JRUFAo5BjWMkE/TAYGRQXw06dAQYGYYwDAS57gXGkymX4XTPqcvz7n/iI1sBOsb3R2dYg%2BffrA398fmZmZ%2BP7777Fy5UrcLOOXW8Efb14FBQVSoVeSSqWq9DELyngjLFnUFp9YZ2hoCJVKhfbt2yMsLKzS8Z9X8kQ9wxcUB4sWLcKIESNw9OhRHD16FDt27MDatWsrnMvi7u6OMWPGYPbs2RgwYACaNm0qPcevv/661JUkbGxsKsy5%2BAzNshgbG5daVlhYiMLCsn85ljXeLxqHV1XW2akii1ThSpwkIMSNG%2BKO8ZIne760mzeBy5fFxM7MFBO3WGwscOmSkNAqlZgCtVhBQaG4Y8gFFqhAUQEssggm0hcsgrWmsxH09PSEvb09IiMjoVar0bZtWzg7OyMpKUnjJK7o6GgoFAo0btwYhoaGePbsmbROrVZrnOT2fJFsZGSksX3xSVgllSy8Y2JiYGhoCAcHBzg7OyMxMREODg5wdHSEo6MjoqOjsXXr1gqfm7W1NerXr48rV65Iy3799dcyt01LS8NHH30ER0dHBAYGYs%2BePWjXrh2OHz9e4XF8fHzQs2dPtG/fHh9//DEAoFGjRjAwMEBGRoaUt7m5OZYuXYqHDx9WGNPR0RGxz3Wrhg4div3798PZ2RmXnytALl26JN2RpTLjTURERKSPdPoxolevXti0aRN69OgBAwMDvPXWW2jUqBHmzJmDGzdu4OzZswgPD0efPn1gYWGBFi1aICMjA1u3bkVycjKWLl2qcTUHExMTPH78GElJSVCpVGjRogUOHjyImJgYxMTESFdPKGnVqlU4c%2BYMoqOjsXjxYgwdOhQmJibo168fcnJyEBYWhvj4eJw4cQJLliypVDdVJpNhxIgRWL16NX7%2B%2BWdcuXIFS5cuLXNbS0tLfP/99/jkk09w584dnD9/HrGxsWjevHmlxzE4OBgXLlyQ7owyaNAgLFq0CFFRUbh16xbmzJmD27dvw8HBocJYw4YNwy%2B//IJNmzbh9u3b2LBhA%2BLi4uDl5YUxY8bg8OHD2Lx5M5KSkvDll1/i%2B%2B%2B/x7BhwwCgUuP9IqampoiLi5PuBU5EREQ6UsOuDqGPdF4E5%2BbmolevXgCKpiasXbsWADB48GDMmDEDb7/9ttTldHJywty5c7Fu3Tr4%2BflBrVaje/fuUrx27drB0dERffv2xfXr1xEQEIDmzZtj5MiRmDlzpnSyV0kBAQEICQlBQEAAlEolZs2aBQDSbfeSkpLg5%2BeH0NBQjBgxAhMnTqzUc/vggw/g5%2BeH6dOnY%2BLEiaXOaCxmZGSEtWvXIjY2Fv369cO0adMwcODAcrcvi7OzM/z9/bFs2TJkZmZi3rx5aN%2B%2BPaZMmYLBgwdDoVBU%2BizJxo0bY82aNdizZw/69OmDw4cPY/369bC3t4enpydWrFiB//znP%2BjTpw/27NmDf/zjH9KJbpUZ7xfx9/fHihUreIk0IiIiqnFkah3ekeKnn37CggULcOzYsTLn%2BxK9KqEnrok%2BMc689NxrnWjVqujKE97e%2Bjcn2NMTOH0a8PHRvznBSiVw8SLQurWwOcFpqU%2BExFUo5NPVdqYAACAASURBVLC2NsOjR8%2BEzQm2ledVvNGrMDAArKyAjAyxc4IreTnOl6bvJ2fpc/6ic6%2BuekfE%2B0%2BJiwfUBjo5MS41NRUXLlzAhg0bMHDgQBbARERERCLVwukLuqaTIvjp06cIDg5Gq1atEBAQoIuQVWrJkiXYvXt3uesnTpxY6o54NUFMTAxGjx5d7vqGDRti//79VZgRERERkX7QSRHctGlTXBL0Z8GqEBgYiJEjR5a7vjLX%2BK0Obm5u2LdvX7nrX3R5NCIiItJj7ARrjVUSiu5uVtnbMtckRkZGGjerICIiIqLKYRFMREREpG/YCdYai2AiIiIifcMiWGscQSIiIiKqddgJJiIiItI37ARrjSNIRERERLUOO8FERERE%2BoadYK2xCCYiIiLSNyyCtcYRJCIiIqJah51gIiIiIn3DTrDWOIJEREREVOuwE0xERESkb9gJ1ppMrVarqzsJooqkpT0VElehkMPa2gyPHj2DSlUo5BiiMPfqwdyrB3OvPvqcv%2BjcbW3r6jxmpSUn6z5mo0a6j1mD8WMEEREREdU6nA5BREREpG84HUJrHEEiIiIiqnXYCSYiIiLSN%2BwEa41FMBEREZG%2BYRGsNY4gEREREdU67AQTERER6Rt2grXGESQiIiKiWoedYCIiIiJ9w06w1lgEExEREekbFsFa4wgSERERUa3DTjARERGRvmEnWGscQSIiIiKqddgJJiIiItI37ARrjUUwERERkb5hEaw1jiARERER1TrsBBMRERHpG3aCtcYRJCIiIqJah51gIiIiIn3DTrDWqn0E8/LysHPnzkpv7%2BrqiqioKCG5pKenY%2BTIkfDw8MDcuXOFHKM88%2BbNw7x58wAAarUa27Ztq9Lji5CcnIwTJ05UdxpERER/PXK57v9pKTc3F8HBwfDy8oKPjw82btxY7ra//vorBg0aBE9PT7z33nu4evWqxvrvvvsOXbt2haenJz788EOkp6drnd/zqr0I3r9/P9avX1/daQAAvv32WyQlJWHfvn1VXgSHhIQgJCQEAHD%2B/Hl8/PHHVXp8EYKDgxETE1PdaRAREVEVWLFiBa5evYrNmzdj4cKFiIiIwKFDh0ptl5WVhQkTJsDLywt79%2B6FUqnExIkTkZWVBQCIiYlBSEgIgoKCsGPHDjx58gTz58/Xeb7VPh1CrVZXdwqSzMxMODk5oWnTplV%2B7Lp160pf16QxISIiohqohk2HyMrKwq5duxAZGQl3d3e4u7sjLi4O27ZtQ48ePTS2PXDgAIyNjTFnzhzIZDKEhPw/e/ceF1WB/nH8M3IXRBGBNAvJddVMEa9pZIbWeklCs5QKQy2tRNPUFPFS4qXU3PW2mZW26lYmoZblJatttS01VOxnYoV3UkC8XwBh5vcHy6wTqEPDESe%2B79eL18g5Z57zzAHh4ZnnnJPAv//9b9avX0%2BvXr1Yvnw5Xbt2JSoqCigqru%2B//36OHDnCbbfdVm45230Ex48fz7PPPmuzLDExkdGjR3P8%2BHFeeOEF2rRpQ9u2bZkyZQr5%2BfkAJCcnExERYfO8mJgY5s2bx9atW4mPjycjI4OGDRty9OhR67piR48eta4rtn37dh588EFCQ0N54YUXOHPmjHXdTz/9RExMDM2aNeMvf/mL3WMF8%2BbNY968eWzfvt06cnH%2B/Hni4%2BNp164dd911F126dGHTpk0AzJo1iyeffNImxuzZs4mNjQXgzJkzTJgwgfbt29OyZUtGjx5tzXPr1q1EREQwadIkWrZsyaJFi6zjEEePHqVfv36A7ejHBx98QEREBGFhYcTExLBv3z67XhfAoUOHGDhwIGFhYXTs2JGlS5da16WnpzNw4EBatGjBvffey/z58zGbzdZjEhMTYxMrIiKC5ORkoOjr%2BMYbbzBw4EDr8d68eTNQNN6xbds25s%2BfXyKGiIiI/LGkpaVRUFBAWFiYdVnLli1JTU211hXFUlNTadmyJSaTCQCTyUSLFi3YtWuXdX2rVq2s29euXZs6deqQmpparjnb3Qnu3r07gwYN4vz58/j4%2BGA2m9mwYQMvv/wyTz31FMHBwSxbtoyTJ08yYcIEoKhwvpawsDDGjRvH4sWLSUpKombNmnbl8s9//pNXX30Vf39/xo0bx/Tp03n11VfJzc3lmWeeoWfPniQmJrJ//34mTJiAt7e39a%2BJqxkwYAAXL15k586dzJs3j%2BrVqzNp0iQOHDjA4sWL8fLy4u233yYhIYEOHTrQvXt33nnnHXJycvD39wdgw4YNPP300wDExcVx6dIl66jHyy%2B/zNixY3njjTcAyMjIID8/n%2BTkZNzc3Jg7dy5Q9IWeN28eQ4cOZcuWLVSvXp0vv/yS%2BfPnk5iYSEhICKtXr6Zfv35s3LiR6tWrX/N15eXlMWDAAJo0acKHH37IkSNHGDlyJLfddhuhoaE8/vjjREREsHLlSg4cOMD48ePx8fGxFvPXs3DhQiZNmsSkSZN4/fXXmTBhAl9%2B%2BSUJCQkcPHjQ%2BhZHWWRlZZGdnW2zzNW1KoGBgWWKYw8Xlyo2j85EuVcM5V4xlHvFceb8nTn36zKgE1za79%2BAgAC7fv9mZ2fj5%2BeHu7u7dVmtWrXIy8vj9OnTNjVednY2f/rTn2ye7%2B/vz88//2zN47f79Pf35/jx42V%2BTddidxHctm1ba0EWGRnJ999/z%2BXLlwHIzMzkww8/tBZkEydO5LnnnmPEiBHXjOnu7k61atVwcXEhICDA7qTj4uK47777gKJCu3///owfP55169bh7%2B/P8OHDAahXrx4ZGRksXbr0ukWwt7c3VatWxc3NzZpL69at6d%2B/P3/%2B85%2BBokJ55cqV5OTk0LhxY%2BrVq8emTZvo06cP%2B/btIyMjgwceeIC0tDS2bdvG%2BvXrCQkJAWDmzJl069aN/fv3W/f59NNPExwcbJOHi4uL9TgW5/H2228zePBg7r//fgCGDx/Ov//9bz7%2B%2BOPrdlm3bNnCyZMnmTZtGj4%2BPjRo0IDx48dTpUoV1q5di5eXF4mJibi6ulK/fn2ys7NZsGCB3UXwfffdR69evQB47rnnePjhh8nOziYoKAg3NzeqVq1KjRo17IpVbMWKFcyfP99m2ZAhQxg2bFiZ4pSFr6%2BXYbGNptwrhnKvGMq94jhz/s6c%2B9VYMJV7zNJ%2B/8bFxTF06NDrPvfSpUs2BTBg/bx4OuB62xZvl5ube8315cXuIrhKlSp07dqV9evXExkZybp163jggQfYv38/9erVs%2BlItmjRgoKCAg4fPlyuyRZr2rSp9d933nmndV/79%2B8nLS3NphVfWFiIi4vL79pPVFQUmzZt4sMPP2T//v3s2bPHGhOgW7dubNy4kT59%2BrBx40bat29PjRo1%2BM9//oOvr6%2B1AAaoX78%2B1atXZ//%2B/db537p169qVR3p6OjNnzmT27NnWZXl5eRw8ePC6zz1w4AAhISH4%2BPhYlz3yyCMATJo0iSZNmuDq%2Br9vg7CwMLKzszl79qxdudWrV8/67%2BJ9FBQU2PXcq%2BnTp0%2BJERpX16qcOnXBobilcXGpgq%2BvF2fPXqKw0Hz9J9xElHvFUO4VQ7lXHGfO3%2Bjc/fy8yz1mRSrt96%2B9TUoPD48SRWrx556ennZtW7zd1dZ7eZXvHzNlOjHuoYceIiYmhvPnz/P5558zc%2BZMfvrppxLbFReJhYWF1nmPK5WlSCqOdaUri9rik8jc3NwoKCigXbt2TJw40e741/LSSy%2Bxc%2BdOHn74YaKjowkICKBPnz7W9d26dePNN9/k7NmzbNy4kYEDBwKU%2BOvlytdy5evx8PCwK4/CwkLGjRtHu3btbJZfWdhezZUF7m%2BVtv/iuR17v3Zubm4ltnH0xL7AwMASb4NkZ5%2BjoMC4H76FhWZD4xtJuVcM5V4xlHvFceb8nTn3qzEb8HJK%2B/1rr6CgIE6dOkVBQYG19sjOzsbT0xNfX98S2544ccJm2YkTJ6z7vtr6skwN2KNMAyWhoaEEBQXx1ltvYbFYaNOmDSEhIRw8eJDTp09bt9u1axeurq7cfvvtuLm5ceHC/zp4FovF5iS33xZa7u7uNtsfOXKkRB5XFt67d%2B/Gzc2NunXrEhISwoEDB6hbty7BwcEEBweza9culi1bVpaXCRRdKWLt2rX89a9/ZdiwYTzwwAPWE9uKi7z69etTv359PvjgAw4ePEjnzp0BCAkJ4ezZszajD7/88gvnz5%2B36Q5fzW%2BPSUhICMePH7e%2BpuDgYBYuXGgdIL%2BWevXqcejQIS5dumRd9tprrzFlyhRCQkLYs2ePdawFYOfOndSsWZMaNWqU%2BNpduHDBkOv0iYiISNmYzeX/4YjGjRvj6upqU5ukpKTQtGlTqvxmfjk0NJSdO3da6ymLxcKOHTsIDQ21rk9JSbFuf%2BzYMY4dO2ZdX17KPFXdrVs3lixZQpcuXXBxceGee%2B7htttu46WXXmLfvn189913JCYm8tBDD%2BHr68tdd93F6dOnWbZsGUeOHGH69Ok2V3Pw8vLizJkzHDx4kIKCAu666y7WrVvH7t272b17t/WEsSv99a9/5dtvv2XXrl1MmTKFvn374uXlRWRkJLm5uUycOJH09HS%2B/vprpk6daj1xrSzc3d3x8vJi48aNHD16lM2bN1uv3Xtli7579%2B688cYbdOjQwdqZrV%2B/Ph06dGDMmDHW1zFmzBhat25tnS%2B%2BluJ2///93/%2BRl5dH//79%2Bcc//sHq1as5fPgwM2fOZN26dXZdyi08PJxatWpZj8kXX3zBBx98QHh4OD169CA/P9%2B6btOmTcybN4/o6GhMJhNNmzYlLS2NdevWceDAASZOnFjiG/laqlatysGDB8nJybH7OSIiIuJ8vLy8iIqK4uWXX2b37t1s2rSJxYsXW694lZ2dTW5uLgBdunTh7NmzTJ06lV9%2B%2BYWpU6dy6dIlunbtCkB0dDRr1qxh5cqVpKWl8dJLL9GxY8dyvTwa/M4iOC8vj27dugFFowl///vfAXjsscd48cUX6dSpk7VgrFevHmPGjOGNN94gKioKi8XCX/7yF2u8u%2B%2B%2Bm%2BDgYHr06MHevXvp378/d955J08%2B%2BSQjR47k%2BeefL5FD//79SUhIoH///oSFhTFq1CigaDzgrbfe4uDBg0RFRTF%2B/HieeOKJMl%2BdAIqK4JkzZ7Jhwwa6d%2B/Oq6%2B%2BynPPPUdAQAB79%2B61OR4XL16ke/fuNs9/7bXXuO2224iNjWXgwIE0aNCABQsW2LXvhg0bcs8999C3b1%2B%2B/vprunXrxogRI5g7dy4PPfQQ3377LW%2B88YbNPO7VuLq68ve//52srCx69uzJ1KlTrd9MPj4%2BvP322xw%2BfJioqCgSExN56qmniIuLA6Bdu3bExsYyceJE%2BvbtS4MGDcr0V9ijjz7K5s2brVfMEBERkfJxs3WCAeLj42nSpAlPPfUUr7zyCkOHDuXBBx8Eippyn332GVBUr7355pukpKTQq1cvUlNTWbRoEVWrVgWKzk%2BaPHkyCxYsIDo6murVqzN9%2BnTHE/wNk6WMA5zffPMNEyZM4Isvvih1ZlTECNnZ5wyJ6%2BpaBT8/b06duuB082LKvWIo94qh3CuOM%2BdvdO4BAdWuv5FB8vLKP6adpyr9Ydh9YlxWVhYpKSm8%2Beab9O7dWwWwiIiISAUx4sS4ysbuIvjcuXOMGzeO5s2b079/fyNzMsTUqVNJSkq66vrBgweXuCOeM2jbtu01r5v36aefUqdOnRuYkYiIiBhNRbDj7C6C69evz86dO43MxVDPPfdcidscX%2Bl6d167WSUlJZW4HeGVjLjLmoiIiIizK9N1gp1ZzZo17b4tszMp7zMlRURE5OanTrDj/oA30xYRERERubZK0wkWERER%2BaNQJ9hxKoJFREREnIyKYMdpHEJEREREKh11gkVEREScjDrBjlMnWEREREQqHXWCRURERJyMOsGOUxEsIiIi4mRUBDtO4xAiIiIiUumoEywiIiLiZNQJdpw6wSIiIiJS6agTLE5h8uRqhsStWxfGjIFFi7w5etSQXTBv1CFjAru7A9745Z6F/Hxj9mFUq8HdHfy88bt42rjcfX2NiWtyAcDPdBmqFBqyi2yzuyFxb4SAQIOOe1gY7NiBX6d7YedOY/YBcPx4%2Bcc0uwLe%2BJlzwVxQ/vGLnT5tTFwPj6L/r2eyIS/PmH3UrGlMXP577MkHDDj22bkQEFD%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%2B%2B/Pjjj9Z1ycnJdO3alWbNmtGrVy%2B2b99uXRcREUFycrL1861bt9KwYUPgf6MdGzdupHPnzjRt2pTBgwdz%2BvRpADp16mR9vDKGiIiIOEadYMepE2yQ7t27M27cOC5fvoybmxv5%2Bfl89dVXxMfH8%2B2331q3S0hI4PLly7z//vvk5eUxZcoUJk%2BezLRp02jXrh3btm3j/vvvJzMzk8OHD9t0V7/55hv69et33Vw2b95MQkICCQkJtG/fnmXLljF48GC%2B%2BOIL1q5dS2JiIpMmTaJZs2YkJyczaNAg1q9fT1BQkF2vdeHChcyePRuLxcJzzz3HkiVLGDFiBCtXruTRRx9l5cqV/PnPf7b72GVlZZGdnW2zrGrVAGrWDLQ7hr2KX6KdL/X3cXc3Jq6rq%2B2jEYz6qejmZvtoBBcXY%2BMaFR9wrWJMf8LFpYrNoyHCwoyJ26iR7aNRjPj/dAO%2BZwDw8DAmbvHPMKN%2BloFxP8eMPvYFBcbEtUNlLFrLm4pgg3To0AGz2czWrVsJDw9ny5YteHp60rZtW%2Bs2hw8fZtOmTWzbto1q1aoBkJiYSFRUFPHx8YSHh/P%2B%2B%2B8D8P3333PPPffw3XffceLECdzc3EhLS%2BPee%2B%2B9bi4rVqzgoYceIjo6GoCXXnoJNzc3zpw5w7Jly4iJiSEqKgqAUaNGsX37dpYvX87IkSPteq3Dhg2jWbNmAPTo0YMffvgBgJo1a1ofPT097YpVnO/8%2BfNtlg0ZEsewYUPtjlFWsbGGhQYMnv925vGawPL/w%2BaG%2Be//WSP4GRa5iK%2Bvl3HBd%2BwwLjbAe%2B8ZG99INWoYG9/f39j4deoYG99Ivr7GxP1Nw0aci4pgg7i7u9O5c2c2btxIeHg4Gzdu5C9/%2BQtVrujwpKenYzab6dChg81zzWYzhw4dIjw8nEmTJnHu3Dm2b9/OPffcw6lTp0hJSQGgYcOG1kLzWg4cOEDfvn1tciseq0hPT2fIkCE22zdv3pz09HS7X2twcLD13z4%2BPly%2BfNnu55amT58%2BRERE2CxbujSA115zKGypgoKKCuB334XMzPKPDzCm3zFjAru6FhXA2dnGdSOM7AQHBkJWFjj4/XJV3t7GxHVxKSqAz52DwkJDdnHKYkyH3MWlCr6%2BXpw9e4nCQmO%2Btn6drv%2BH%2Be/SqFFRAfz445CWZsw%2BAD7/vPxjurgUFcCnTxv2PQMUfU8awd29qAD%2B9VfIzzdmH9WrGxPXxaWoAD571thjXwGcsRNssVh4/fXXSUpKwmw207t3b0aNGmVTG11p165dvPrqq%2Bzbt4/AwECefvppHn30Uev6yMhI9u3bZ/OcTz75xO53n1UEG6hbt27Ex8czfvx4vvzySxYsWGCzvrCwkGrVqvHRRx%2BVeG5QUBCenp4EBwfz/fff8/3339OzZ09%2B/fVXduzYQV5enl1dYADXa7zN5FHK22eFhYWYr/K/q7CUHyJu5fyWdmBgIIG/6RBevFj0YZTMTDDsqnVG/dIoVlBg3D6M/il7%2BbJxuZfh3YffpbDQsF%2BqBWZj3zYvLDRTUGDQ13bnTmPiFktLM3YfRr69XVhobPy8PONiQ9H/VaP2YfRYgdHHXuyyZMkS1q5dy/z58ykoKGD06NH4%2B/szcODAEttmZ2fzzDPPEB0dzauvvsqePXuIj48nICCAjh07UlhYyMGDB1m%2BfDn16tWzPs/Pz/730nRinIHat29PYWEhS5YswdPTk1atWtmsDwkJ4dy5c5hMJoKDgwkODiY3N5cZM2aQ/9/CIDw8nE2bNpGRkcGdd95Jq1atSElJYcuWLXYXwcHBwaRd0TkpLCwkIiKClJQUQkJCSE1Ntdk%2BNTWVkJAQoKjAvXDhgnXdkSNH7H79JpPJ7m1FRETEfs54YtzSpUsZNmwYrVq14u6772bUqFH885//LHXbTZs2UatWLV588UXq1atH9%2B7diYqK4pNPPgGKTs6/fPkyzZo1IyAgwPpxrcbfb6kINpCrqysPPvggCxcupEuXLiWKwvr163PvvfcyatQodu/ebf0r5%2BLFi/j%2Bd34pPDycNWvW0LRpU9zc3GjVqhV79uzh9OnTNG/e3K48YmJi%2BPjjj1m1ahWHDh1i%2BvTpWCwWmjRpQmxsLMuXL2f16tUcOHCAWbNmkZaWRu/evQFo2rQpSUlJ/PTTT2zdupXFixfb/fq9vIrmDtPS0mwKaREREXGMsxXBmZmZHDt2jNatW1uXtWzZkoyMDLKyskpsf%2B%2B99zJ9%2BvQSy8%2BfPw/AL7/8Qu3atUt9R9teKoIN1r17dy5evEj37t1LXT9jxgzq1q1LbGws/fv3JyQkhNmzZ1vXt2nTBpPJRMuWLQGoVasWt99%2BO%2B3bt7f7r53WrVszadIkFixYQGRkJHv37mXhwoV4enrSrVs3RowYwdy5c4mMjGTbtm0sXryY%2BvXrAzB8%2BHB8fX3p1asXU6dO5YUXXrD7tdesWZPIyEiGDx/OypUr7X6eiIiI3HhZWVns2bPH5qO0AvX3KL7q05XjjrVq1QLg%2BPHjJbavW7euTbMvJyeHTz/9lHbt2gFF5zS5ubkxePBg7rnnHp588kl2795dppxMFt3RQJzAUIMuDFG3LowZA6%2B9ZtxM8LxRh4wJ7O4OtWvDsWPONxPs7g633goZGcblbtTZ4DfgJKdsszGXonJ1rYKfnzenTl0wbCY4INCg4x4WVnTliRYtjJ0JLuWXscNcXYuu3JCTY%2Bxc6n%2Bv0V7uPDygXj04eNC4mWA7TvL%2BXVxdwc8PTp0y7thX0BV6rriNQLn5v/%2BbV%2BLqTHFxcQy185dwbm4umVc5yzwzM5OYmBjS0tKs74ybzWYaN27MP//5zxIjo7%2BNO2DAAHJycli9ejVeXl7Ex8fz1VdfMWXKFGrXrs2HH37Ixx9/zGeffWb3XXl1YpyIiIiIlHp1prLc5TY1NfWq9y8YPXo0APn5%2BdYRhuLzn4rHJ0tz4cIFnn/%2BeQ4ePMh7771n3bb45mI%2BPj4AvPzyy%2BzYsYM1a9bw7LPP2pWvimAnt2HDBsaOHXvV9S1btuTtt9%2B%2BgRmJiIiI0Yx4o660qzOVRdu2bUtcsqxYZmYmM2fOJDs7m7p16wL/G5G4WqF9/vx5nn76aQ4fPsw//vEPm6tAuLq6WgtgKDoZ/4477rhqJ7o0KoKdXHh4OKtXr77q%2BrLcpEJEREScg7NdJzgoKIg6deqQkpJiLYJTUlKoU6dOqYW32WwmLi6Oo0ePsmzZMuu5SsViYmJo27YtcXFx1u337dvHE088YXdOKoKdnLe3N95G3RRAREREpJxER0cza9YsbrnlFgBef/11BgwYYF1/8uRJPDw88Pb2Jikpia1bt/LGG2/g6%2Btr7Rq7ublRo0YNIiIiWLBgAY0bNyYkJISlS5dy7tw5evbsaXc%2BKoJFREREnIyzdYIBBg4cSE5ODnFxcbi4uNC7d29iY2Ot63v37k3Pnj0ZOnQoGzZswGw2M3jwYJsYbdq0YdmyZcTGxpKXl8eUKVM4ceIEoaGhLFmyxGZE4npUBIuIiIiI4VxcXIiPjyc%2BPr7U9V9%2B%2BaX13%2B%2B88841Y5lMJp599lm7T4IrjYpgERERESfjjJ3gm42KYBEREREnoyLYcbpjnIiIiIhUOuoEi4iIiDgZdYIdp06wiIiIiFQ66gSLiIiIOBl1gh2nIlhERETEyagIdpzGIURERESk0lEnWERERMTJqBPsOHWCRURERKTSMVksFktFJyFyPXl5xsQ1mcDdHfLzwaj/CR44cfJGtRpMJvD0hNxc43L39DQmLhTlb%2BCPzuwT5w2J6%2BpaBT8/b06dukBBgTFf2wDzRUPi4uoK/v6QkwMFBcbsA%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%2BjRo2nRogXh4eEkJiaSm5tr937WrFlDly5dCA0NpW/fvvz444/WdcnJyXTt2pVmzZrRq1cvtm/fbl332xGNrVu30rBhQ%2BB/Yx0bN26kc%2BfONG3alMGDB3P69GkAOnXqZH38o415iIiIiHNTJ/gmdODAAQYPHszQoUPp3bs3AAkJCVy%2BfJn333%2BfvLw8pkyZwuTJk5k2bdp1423evJmEhAQSEhJo3749y5YtY/DgwXzxxResXbuWxMREJk2aRLNmzUhOTmbQoEGsX7%2BeoKAgu/JduHAhs2fPxmKx8Nxzz7FkyRJGjBjBypUrefTRR1m5ciV//vOf7X79WVlZZGdn2yyrXj2AwMBAu2PYy2SyfTSGQcFvRPJGxb4xB95puboa059wcali82gIs0G/VlxcbB%2BNYsR4WqNGto9GsfNndpn5%2B9s%2BGqFBA2Pi3n677WN5%2B/lnY%2BLaQZ1gx6kIvsmcOHGCp59%2Bmscee4wBAwYAcPjwYTZt2sS2bduoVq0aAImJiURFRREfH29ddjUrVqzgoYceIjo6GoCXXnoJNzc3zpw5w7Jly4iJiSEqKgqAUaNGsX37dpYvX87IkSPtynnYsGE0a9YMgB49evDDDz8AULNmTeujp6en3cdgxYoVzJ8/32bZkCFxDBs21O4YZeXmZlhowN3I4EYnbywPj4rO4PczsID38/M2LDaAr6%2BXgdGNzZ0aNYyNv2OHcbHfe8%2B42DfCww8bF/u/v%2B8MM368MXHvv9%2BYuHZQEew4FcE3mblz51JQUMAtt9xiXZaeno7ZbKZDhw4225rNZg4dOsRdd911zZgHDhygb9%2B%2B1s/d3d0ZM2aMNfaQIUNstm/evDnp6el25xwcHGz9t4%2BPD5cvX7b7uaXp06cPERERNsuqVw8gP9%2BhsKUymYpqyMuXwagRZncMSBxuTPJG/ZQ1mYoK4Lw843I3ssA2mYzLGzh1%2BqIhcV1cquDr68XZs5coLDTma%2Btntn9Mq0xcXIoK4NOnobDQmH0APPBA%2Bcds1KioAH78cUhLK//4xeLijInr719UAK9ZA0Zd7ee774yJe/vtRQXwlClw%2BLAx%2BxCnpSL4JtOxY0fatGnD3/72N7p06ULNmjUpLCykWrVqfPTRRyW2t2dkwdX16l9mj1IKhcLCQsxXKX4KS/nl41bOncjAwMASow9G1kpQFNu4%2BAafIGhk8kaf3GjsgXdaBQXGtngKC83G7cNcYEzcYoWFUGDgPnbuNC52Wpqx8TMzjYsNRQWwUfsweqzg8OEKHV0wgjrBjtOJcTeZiIgInnjiCYKCgpg5cyYAISEhnDt3DpPJRHBwMMHBweTm5jJjxgzy7WiPBgcHk3ZF96GwsJCIiAhSUlIICQkhNTXVZvvU1FRCQkKAogL3woUL1nVHjhyx%2B7WYNO8pIiIiNykVwTchFxcXxo8fz6pVq9i5cyf169fn3nvvZdSoUezevZs9e/YQHx/PxYsX8fX1vW68mJgYPv74Y1atWsWhQ4eYPn06FouFJk2aEBsby/Lly1m9ejUHDhxg1qxZpKWlWU/Ia9q0KUlJSfz0009s3bqVxYsX2/06vLyK5g7T0tJsCmkRERFxjG6W4TgVwTeptm3b8uCDDzJ58mQKCwuZMWMGdevWJTY2lv79%2BxMSEsLs2bPtitW6dWsmTZrEggULiIyMZO/evSxcuBBPT0%2B6devGiBEjmDt3LpGRkWzbto3FixdTv359AIYPH46vry%2B9evVi6tSpvPDCC3a/hpo1axIZGcnw4cNZuXLl7zoOIiIiUpKKYMeZLLqjgTiBvDxj4ppM4O4O%2BfkGnp%2BFEydv5Ilxnp6Qm2tc7mW4IkmZGXxiXPaJ84bEdXWtgp%2BfN6dOXTBsJjjAbMxJfbi6Fp2glZNj7EzwFScll5uwsKKrTrRoYexMsB2XzPxdgoKKrt6weLFxM8EbNxoTt0EDWLQIBg0ybib4q6%2BMiXsd06eXf8z4%2BPKPeSWLxcLrr79OUlISZrOZ3r17M2rUKKpUKb0nO2XKFJYtW2azbMKECTz55JMArF27lr/97W9kZ2db76FQfGUqe%2BjEOBEREREn44yd2yVLlrB27Vrmz59PQUEBo0ePxt/fn4EDB5a6fXp6OiNHjqRnz57WZT4%2BPgDs3r2bhIQEXnnlFRo1asTUqVOJj4/nzTfftDsfFcFObsOGDYwdO/aq61u2bMnbb799AzMSERERKWnp0qUMGzaMVq1aAUX3JpgzZ841i%2BCBAwcSEBBQYt3y5cvp2rWr9T4HM2bM4P777%2BfIkSPcdtttduWjItjJhYeHs3r16quuL8tNKkRERMQ5OFsnODMzk2PHjtG6dWvrspYtW5KRkUFWVlaJS6OeP3%2BezMxM6tWrV2q81NRUnnnmGevntWvXpk6dOqSmpqoIriy8vb3x9jb4Dk0iIiJyU3G2Ijg7OxvAptitVasWAMePHy9RBKenp2MymVi4cCH//ve/qVGjBv3797eORpRWOPv7%2B3P8%2BHG7c1IRLCIiIiJkZWVZi9ViAQEBJYrNq8nNzSXzKidPXrxYdNKsu7u7dVnxv0u758H%2B/fsxmUzccccdPPnkk2zfvp0JEybg4%2BPDAw88QG5urk2s4nj23D%2BhmIpgERERESdjRCd4xYoVzJ8/32ZZXFwcQ4cOtev5qamp9OvXr9R1o0ePBooK3uK71RYXrMX3FbhSVFQU999/PzVq1ACgUaNGHDx4kPfff58HHngADw%2BPEgVvfn5%2BqbGuRkWwiIiIiNCnTx8iIiJslpV2UtrVtG3bln379pW6LjMzk5kzZ5KdnU3dunWB/41IlLYPk8lkLYCL3XHHHXz33XcABAUFceLECZv1J06cKFO%2BKoJFREREnIwRneDAwEC7Rx/KKigoiDp16pCSkmItglNSUqhTp06p%2B5wzZw47d%2B7k3XfftS5LS0vjjjvuACA0NJSUlBR69eoFwLFjxzh27BihoaF256QiWERERMTJONuJcQDR0dHMmjWLW/57U5rXX3%2BdAQMGWNefPHkSDw8PvL29uf/%2B%2B1m0aBHvvPMODzzwAFu2bGH16tUsXbrUGismJobmzZvTtGlTpk6dSseOHe2%2BMgSoCBYRERGRG2DgwIHk5OQQFxeHi4sLvXv3JjY21rq%2Bd%2B/e9OzZk6FDh9KsWTPmzJnD3LlzmTNnDrfeeiuvv/46YWFhAISFhTF58mTmzp3LmTNnuOeee0hMTCxTPiqCRURERJyMM3aCXVxciI%2BPJ/4q92f%2B8ssvbT7v3LkznTt3vmq8Xr16Wcchfo/Sb9YsIiIiIvIHpk6wiIiIiJNxxk7wzUZFsIiIiIiTURHsOI1DiIiIiEilo06wiIiIiJNRJ9hxKoLFKVQx%2BD0Lk6nowxC59t/HvEyqVAF3d7h82bifhkbFrVIFPD2Nzd3T05i4cm2nTxsT18MD/P3h3DnIyzNmHwDTppV/zKCgose4OMjMLP/4xcaNMyZuWBgMGADz58POncbsIznZmLjVqxc99u0LZ84Ysw9xWiqCRURERJyMOsGOUxEsIiIi4mRUBDtOJ8aJiIiISKWjTrCIiIiIk1En2HHqBIuIiIhIpaNOsIiIiIiTUSfYcSqCRURERJyMimDHaRxCRERERCoddYJFREREnIw6wY5TJ1hEREREKh11gkVEREScjDrBjlMRLCIiIuJkVAQ7TuMQIiIiIlLpqBMsIiIi4mTUCXacOsEiIiIiUumoEywiIiLiZNQJdpyKYBEREREnoyLYcRqHEBEREZFKR0Wwg%2BbNm0dMTAzJyclERERcd/srt9u6dSsNGzYE4OjRozRs2JCjR48amu%2BNlJ%2Bfz4cffljRaYiIiPzhmM3l/1HZqAguJ926dSMpKalMzwkLC2PLli0GZVTxPv30UxYuXFjRaYiIiPzhqAh2nGaCy4mnpyeenp5leo67uzsBAQEGZVTxLBZLRacgIiIiUip1gsvol19%2BITo6mtDQUPr168epU6cASoxDzJ49m/DwcJo1a0ZMTAw///xziVhXjkP81rJly2jVqhV79%2B4F4KeffiImJoZmzZrxl7/8hX/%2B859251xQUGDNp2XLlgwbNsyad15eHjNnzuS%2B%2B%2B6jefPmPPvssxw7dgwofUSjePyj%2BDXHxMQwd%2B5c2rZtS6tWrZg%2BfToWi4WtW7cSHx9PRkbGH27MQ0REpKKpE%2Bw4dYLLID8/n0GDBtGqVSumTJnCd999x7Rp02jRooXNdp9//jkrVqxgwYIFBAYG8te//pX4%2BHi7xyXWr1/P7Nmzeeutt2jcuDG5ubk888wz9OzZk8TERPbv38%2BECRPw9vYmKirquvHmzJnD6tWrmTZtGnXq1GHSpElMmjSJuXPnMmnSJHbs2MFrr71GjRo1mDVrFs8//zwfffSRXbnu3LmTWrVq8f777/PDDz8wduxYOnToQOvWrRk3bhyLFy8mKSmJmjVr2hUPICsri%2BzsbJtlfn4BBAQE2h3jplLFoL81i%2BMaFd9Izpz7DeDqasxxcXGpYvNoCA8PY%2BK6u9s%2BGiUoqPxj%2BvvbPholLMyYuI0a2T4aoXp1Y%2BL6%2BNg%2BlrczZ4yJKzeEiuAy%2BM9//sPp06d5%2BeWXqVq1KvXr12fbtm2cPHnSZruMjAzc3NyoU6cOderUYcKECezfv9%2BufXz//fe88sor/PWvf6VVq1YAfPLJJ/j7%2BzN8%2BHAA6tWrR0ZGBkuXLr1uEWyxWPjwww8ZM2YMHTp0AOCVV15h3bp1nDlzhjVr1vDWW29x9913AzBr1iw6duzIN998Q0hIyHXzLSwsJDExER8fH%2B644w7effddfvjhB%2B655x6qVauGi4tLmUc%2BVqxYwfz5822WDRkSx7BhQ8sUpyzc3AwLDW7eBgYHvLyMjW8kb4OPjZFMJsNC%2B/kZe1x8fQ38njE4d%2BrUMTb%2BgAHGxX74YeNig7G5A7z3nrHxjdSmjTFxV60yJq4dKmPntrypCC6DX375hXr16lG1alXrsqZNm/L111/bbNe9e3eWL19Op06daN68OZ07d6Z379527WPixIkUFhZSu3Zt67L9%2B/eTlpZG2BV/5RcWFuLi4nLdeKdOneL06dM0adLEuuxPf/oTQ4cOJTU1FbPZTGhoqHVdjRo1CAkJIT093a4i2N/fH58r/sL28fGhoKDgus%2B7lj59%2BpS40oafXwCXLzsU9qrc3DAsNoBb/gVjAlepUlQAX7pk3E9Do%2BJWqVJUAF%2B4YNw%2BjOr8QFEBbODM%2B6nTFw2J6%2BJSBV9fL86evURhoTHH3e9M9vU3%2Bj3c3YsK4F9/hfx8Y/YB8OWX5R/T37%2BoAF6zBnJyyj9%2Bsd80D8pNo0ZFBfDjj0NamjH7mDXLmLg%2BPkUF8LZtcP68MfuoICqCHaciuIx%2Be7KXWyktxICAANatW8c333zDV199xTvvvMOHH37I6tWrrxv/xRdfZMeOHUyePNk691tQUEC7du2YOHFimfN1db36l9jjKm9bFhYWYjabMZXS6fptgeteyluTjp4QFxgYSGCg7eiDkUWq4Yz%2BSWXkMJcz5%2B7ECgqMPSaFhWbj9pGXZ0zcYvn5xu4jM9O42Dk5xsbfudO42FBUABu1D6PHCs6f1%2BiClKCBvDJo0KABBw8e5Ny5c9ZlxSeuXelf//oXK1eupGPHjrzyyiusWbOGgwcP8tNPP113H507d2bMmDH83//9n7VoDgkJ4cCBA9StW5fg4GCCg4PZtWsXy5Ytu248X19f/Pz8SLvir/e9e/fSoUMH6tati6urK7t27bKuO3XqFIcOHSIkJMRa4F%2B48L9OZllOcCutiBYRERHH6cQ4x6kILoP27dtTu3ZtEhISSE9PJzk5mc8%2B%2B6zEdmazmRkzZvD5559z9OhRkpOT8fLyol69enbt59Zbb%2BXpp59m5syZnDt3jsjISHJzc5k4cSLp6el8/fXXTJ06FX87T7KIiYlhzpw5fPfdd/z8889MnTqV5s2b4%2BPjw6OPPkpiYiJbt24lLS2N0aNHc8stt3DPPfdQq1YtateuzTvvvMORI0dITk7mX//6l93Hy8vLizNnznDw4EGHRyREREREypOK4DJwc3PjzTff5MyZM/Ts2ZP333%2BfJ554osR2ERERDBs2jOnTp9O1a1c%2B%2B%2Bwz/v73v1O9DGe/PvPMM7i7uzNnzhx8fHx46623OHjwIFFRUYwfP54nnniCwYMH2xVr0KBBPPjggwwfPpzo6GhuueUWEhMTARgzZgzt27dn2LBhREdH4%2BHhwbvvvou7uztVqlRh6tSp7N69m27durF%2B/XqeffZZu1/D3XffTXBwMD169Ci1Yy4iIiK/jzrBjjNZdEcDcQKGnrhm9Ilxueeuv9HvcSNOLjPyxLhq1eDcOeP24etrTFww/MS47BPGnMDj6loFPz9vTp26YNhMcMDJXw2Ji4cH1KsHBw8aOxOcnFz%2BMYOCiq7csHixsTPB48YZEzcsDHbsgBYtjJsJNuK4Q9Gl1yIiik54NGomuGdPY%2BJeR48e5R/zk0/KP%2BbNTJ1gEREREal0dHUIJzd16tRr3oRj8ODBZRphEBERkZtfZRxfKG8qgp3cc889x5NPPnnV9WWZQxYRERGpLFQEO7maNWuW6ZbEIiIi4vzUCXacimARERERJ6Mi2HEqgkVERETEcBaLhddff52kpCTMZjO9e/dm1KhRVKlS8joNY8eOZdWqVSWWt23blqVLlwLQqlUrmxuYAezYsQNvb2%2B78lERLCIiIuJknLETvGTJEtauXcv8%2BfMpKChg9OjR%2BPv7M3DgwBLbJiQkMHLkSOvnGRkZxMTE0K9fPwAyMzM5d%2B4cmzZtwtPT07pd1apV7c5HRbCIiIiIGG7p0qUMGzaMVq1aATBq1CjmzJlTahFcrVo1qlWrZv187NixdOnShc6dOwOQnp5OQEAAt9122%2B/OR0WwiIiIiJNxtk5wZmYmx44do3Xr1tZlLVu2JCMjg6ysLAIDA6/63G%2B//Zbt27ezYcMG67JffvmFkJAQh3JSESwiIiLiZIwogrOyssjOzrZZFhAQcM0C1V7Fca%2BMVatWLQCOHz9%2BzX0sWrSInj17Urt2beuy9PR0Ll26RExMDAcOHKBx48aMGzeuTIWximARERERYcWKFcyfP99mWVxcHEOHDrXr%2Bbm5uWRe5dbgFy9eBMDd3d26rPjf%2Bfn5V4155MgRvvvuOxISEmyW79%2B/nzNnzvDiiy/i4%2BPDW2%2B9RWxsLJ9%2B%2Bik%2BPj525asiWERERMTJGNEJ7tOnDxERETbLAgIC7H5%2Bamqq9cS13xo9ejRQVPB6eHhY/w3g5eV11ZgbNmygcePG/OlPf7JZ/s4773D58mXrlSBmzZrFfffdx1dffUWPHj3syldFsIiIiIgQGBjo0OhD27Zt2bdvX6nrMjMzmTlzJtnZ2dStWxf434jEtQrtzZs306lTpxLL3d3dbbrKHh4e1K1b96qd6NKUvDCbiIiIiNzUzOby/zBSUFAQderUISUlxbosJSWFOnXqXLXwtlgs/PDDD7Ro0aLE8s6dO5OcnGxddvHiRQ4dOsQdd9xhd07qBIuIiIg4GWe7OgRAdHQ0s2bN4pZbbgHg9ddfZ8CAAdb1J0%2BexMPDwzrikJGRwYULF0qMQphMJjp27Mi8efO49dZbqVmzJnPmzOGWW27hvvvuszsfFcEiIiIiYriBAweSk5NDXFwcLi4u9O7dm9jYWOv63r1707NnT%2BuJeDk5OQBUr169RKzRo0fj6urKyJEjOX/%2BPHfffTeLFi3CxcXF7nxMFovF4thLEjFeXp4xcU0mcHeH/Hww6n%2BCh/mSMYFNJvD0hNxc45I3yo3I/Yo7CJU7k8nQY5594rwhcV1dq%2BDn582pUxcoKDCmjRRAriFxcXUFPz84dQoKCozZB8Bjj5V/zAYNYNEiGDQIfv65/OMXGzbMmLjVq0NEBHz5JZw5Y8w%2BevUyJm5YGOzYAS1awM6dxuyjgn7%2BtmtX/jG//bb8Y97MNBMsIiIiIpWOxiFEREREnIwzzgTfbFQEi4iIiDgZFcGO0ziEiIiIiFQ66gSLiIiIOBl1gh2nTrCIiIiIVDrqBIuIiIg4GXWCHaciWERERMTJqAh2nMYhRERERKTSUSdYRERExMmoE%2Bw4dYJFREREpNJRJ1hERETEyagT7DgVwSIiIiJORkWw4zQOISIiIiKVjjrBIiIiIk5GnWDHqQgWERERcTIqgh2ncQgRERERqXTUCRYRERFxMuoEO06d4FJ88cUXdOjQgdDQUDZv3nzD9tuwYUO2bt0KQE5ODuvWrbth%2BzbKunXryMnJqeg0RERERGyoCC7F3LlzCQ8P57PPPqN169Y3bL9btmwhLCwMgFmzZvH111/fsH0bISMjg%2BHDh3Pp0qWKTkVEROQPxWwu/4/KRuMQpTh37hwtW7bk1ltvvaH7DQgIsP7bYrHc0H0b4Y/wGkRERG5GlbFoLW/qBP9GREQEGRkZjBs3joiICFJSUoiOjiY0NJTmzZvzzDPPkJWVhdls5t577%2BWjjz6yPtdisdChQwfWrFkDwM6dO4mOjqZ58%2BZERETw/vvvW7cdO3YsY8eOJTIyknbt2nHw4EHrOMS8efNYtWoVq1atIiIiAoCzZ88yevRoWrRoQXh4OImJieTm5tr9utasWUOXLl0IDQ2lb9%2B%2B/Pjjj9Z1ycnJdO3alWbNmtGrVy%2B2b99uczySk5Otn2/dupWGDRsCcPToURo2bMjGjRvp3LkzTZs2ZfDgwZw%2BfRqATp06WR%2BvjCEiIiJS0dQJ/o2kpCR69uzJgAED6Ny5Mw8//DCxsbHMmDGDrKwsxo0bx6JFixg/fjxdunTh888/55FHHgFg165dnD59mk6dOpGens5TTz1FbGwsU6dOJTU1lVdeeYVatWrxwAMPAEWF6YIFC6hVqxb16tWz5jBgwADS09MBmDhxIgAJCQlcvnyZ999/n7y8PKZMmcLkyZOZNm3adV/T5s2bSUhIICEhgfbt27Ns2TIGDx7MF198wdq1a0lMTGTSpEk0a9aM5ORkBg0axPr16wkKCrLrmC1cuJDZs2djsVh47rnnWLJkCSNGjGDlypU8%2BuijrFy5kj//%2Bc92fw2ysrLIzs62WVa9egCBgYF2x7CXyWT7aAijgt%2BQ5A3izLnfAK6uxvQnXFyq2Dwaw6BfKy4uto9GadCg/GPefrvto1GqVzcmro%2BP7aMR/jsKWO4aNbJ9LG87dxoT1w7qBDtORfBv1KxZExcXF6pVq4a7uzvPP/88/fv3x2Qycdttt/Hggw%2Bye/duALp3705MTAznz5/Hx8eHDRs2cN999%2BHj48O8efO48847efHFFwG44447SE9P5%2B2337YWwU2bNrV2eq/k7e2Np6enNZ/Dhw%2BzadMmtm3bRrVq1QBITEwkKiqK%2BPh467KrWbFiBQ899BDR0dEAvPTSS7i5uXHmzBmWLVtGTEwMUVFRAIwaNYrt27ezfPlyRo4cadcxGzZsGM2aNQOgR48e/PDDD9bcix%2BLX489VqxYwfz5822WDRkSx7BhQ%2B2OUVZuboaFBux/7b%2BLh4ex8Y3kzLkbWMD7%2BXkbFhvA19fLwOjG5o6vr7HxFy0yLvb48cbFvhHatDEu9o4dxsUGeO89Y%2BLqD3mnpiL4GgICAoiKiuLdd99l7969/PLLL%2Bzbt48WLVoA0Lx5cwICAvj666/p3r07GzduZPTo0QCkp6dbC8NiYWFhfPDBB9bP7Z05Tk9Px2w206FDB5vlZrOZQ4cOcdddd13z%2BQcOHKBv377Wz93d3RkzZow19pAhQ2y2b968ubUTbY/g4GDrv318fLh8%2BbLdzy1Nnz59SvxxUL16APn5DoUtlclUVABfvgxGjTC7m%2B0fWykTk6moiMzLMy55o9yI3I0ssE0mQ4/5qdMXDYnr4lIFX18vzp69RGGhMW0kPwz4jwpFHWBfXzh7FgoLjdkHwH9/Npar228vKoCnTIHDh8s/frErfs6XKx%2BfogJ42zY4f96YfYwaZUzcRo2KCuDHH4e0NGP2UUHUCXaciuBryMzM5JFHHqFJkya0b9%2Bexx57jH/961%2BkpqZat%2BnWrRsbNmwgODiYU6dO0bFjRwA8SvkFbDabKbzih3dp25SmsLCQatWq2cwfF7NnZMHV9epf5tJyKCwsxHyV/12FpfzycSvnNmpgYGCJ0Qej6zyLxcD4RheohiZvMGfO3UAFBcb%2BdissNBu4jwKD4v5XYSEUGLiPn382Lvbhw8bGP3PGuNhQVAAbtQ%2BjxwrS0ip0dMEIKoIdpxPjruHzzz%2BnevXqvPnmmzz11FO0atWKI0eO2Fz1oHv37nzzzTds2LCBiIgIvLyK3mYMCQmxKZah6ES5kJAQu/ZtuuItlpCQEM6dO4fJZCI4OJjg4GByc3OZMWMG%2BXa0R4ODg0m74i/gwsJC60l/peWZmppqzdPNzY0LFy5Y1x05csSu/H/7GkRERERuJiqCr6FGjRr8%2BuuvfPvttxw5coRFixaxceNGm8KzcePGBAYGsnz5crp27Wpd/vjjj7N3715mz57NgQMHWLVqFe%2B99x5PPPGEXfv28vIiIyODzMxM6tevz7333suoUaPYvXs3e/bsIT4%2BnosXL%2BJrx3xcTEwMH3/8MatWreLQoUNMnz4di8VCkyZNiI2NZfny5axevZoDBw4wa9Ys0tLS6N27N1A0t5yUlMRPP/3E1q1bWbx4sd3Hr/gPgrS0NJtCWkRERByj6wQ7TkXwNXTt2pXIyEiGDRvGI488wtatWxkzZgzp6ek2hXC3bt1wcXGxmdmtU6cOb775Jps3b6ZHjx688cYbjB071noliet5%2BOGHOXDgAJGRkVgsFmbMmEHdunWJjY2lf//%2BhISEMHv2bLtitW7dmkmTJrFgwQIiIyPZu3dYqw95AAAgAElEQVQvCxcuxNPTk27dujFixAjmzp1LZGQk27ZtY/HixdSvXx%2BA4cOH4%2BvrS69evZg6dSovvPCC3cevZs2aREZGMnz4cFauXGn380RERESMZrLojgbiBPLyjIlrMoG7O%2BTnG3h%2BltmgO%2BaZTODpCbm5zjdXeyNyL8MVScrM4BPjsk8Yc/KRq2sV/Py8OXXqgmEzwQEYdCKoqyv4%2BcGpU8bOBD/2WPnHbNCg6KoTgwYZOxM8bJgxcatXh4gI%2BPJL42aCe/UyJm5YWNGVJ1q0MG4muIJ%2B/l5xf61y85urk/7h6cQ4ERERESdTGccXypuKYCe3YcMGxo4de9X1LVu25O23376BGYmIiIjc/FQEO7nw8HBWr1591fVluUmFiIiIOAd1gh2nItjJeXt74%2B1t8B2aRERERP5gVASLiIiIOBl1gh2nIlhERETEyagIdpyuEywiIiIilY46wSIiIiJORp1gx6kTLCIiIiKVjjrBIiIiIk5GnWDHqQgWERERcTIqgh2ncQgRERERuWEsFgsDBgwgOTn5mtsdOXKE2NhYmjdvTrdu3diyZYvN%2Bv/85z889NBDhIaG0q9fP44cOVKmPFQEi4iIiDgZs7n8P25M3mamTJnCN998c83tLBYLQ4YMoVatWnz00Uc8/PDDxMXF8euvvwLw66%2B/MmTIEHr16kVSUhI1a9bk%2Beefx2Kx2J2LimARERERMVxmZiZPPfUUX375Jb6%2Bvtfc9rvvvuPIkSNMnjyZ%2BvXrM3jwYJo3b85HH30EwMqVK7nrrrsYMGAADRo0YPr06WRkZLBt2za781ERLCIiIuJknLETvGfPHmrXrs1HH31EtWrVrrltamoqd955J1WrVrUua9myJbt27bKub9WqlXWdl5cXTZo0sa63h06MExEREXEyRhStWVlZZGdn2ywLCAggMDCwXOJHREQQERFh17bZ2dkl9uvv78/x48ftWm8PFcHiFDw8jImblZXFihUr6NOnT7n9Jy/Jy5CoWVlZrHjrLYNzN4bT527w90xAwLU7JL9XVlYW8%2BYtNvi4G5f7innzjP%2Be%2Beqrcg9pzX3KFKf7focbdOzLMMdZFtbc1693ymN/LUYcsnnzVjB//nybZXFxcQwdOtSu5%2Bfm5pKZmVnquoCAAJuu7vVcunQJd3d3m2Xu7u7k5%2Bfbtd4eGoeQSi07O5v58%2BeX%2BMvXGSj3iqHcK4ZyrzjOnL8z514R%2BvTpQ3Jyss1Hnz597H5%2BamoqDz74YKkf//nPf8qUi4eHR4mCNj8/H09Pz2uu9/Kyv/GkTrCIiIiIEBgY6FDHvG3btuzbt69ccgkKCuKXX36xWXbixAlrfkFBQZw4caLE%2BsaNG9u9D3WCRUREROSmEhoayp49e8jNzbUuS0lJITQ01Lo%2BJSXFuu7SpUv8%2BOOP1vX2UBEsIiIiIhXu5MmTXLhwAYA2bdpQu3Zt4uPj%2Bfnnn1m0aBG7d%2B%2Bmd%2B/eADzyyCPs2LGDRYsW8fPPPxMfH0/dunVp27at3ftzefnll1824oWIOAtvb2/atGmDt7d3RadSZsq9Yij3iqHcK44z5%2B/Muf%2BR/eMf/6BNmzY24wvdu3cnNzeXtm3bUqVKFcLDw/nkk0%2BYO3cumZmZvPrqq9x5550A%2BPr6cuedd7Jo0SIWL16Mn58fs2bNonr16nbnYLKU5dYaIiIiIiJ/ABqHEBEREZFKR0WwiIiIiFQ6KoJFREREpNJRESwiIiIilY6KYBERERGpdFQEi4iIiEiloyJYRERERCodFcEiIiIiUumoCBYRERGRSkdFsIiIiIhUOiqCRZxEv379OHv2bInlJ0%2BepFevXhWQUdk4c/7z58/n0qVLJZafP3%2BeV199tQIysp8zH/fVq1eTn59fYvnFixdZunRpBWRkP2fO/ddff8VisZRYXlhYyJ49eyogI/s583GXG8%2B1ohMQuREiIiIwmUx2bfvFF18YnI39/v3vf7N7924Atm/fzsKFC6latarNNocOHSIjI6Mi0rsuZ85///795OTkALBgwQIaNWpE9erVbbb56aef%2BOCDDxg7dmxFpHhVznzcT548SW5uLgDx8fE0aNAAPz8/m2327t3LrFmz6NevX0WkeFXOnPuVOnXqxDfffEPNmjVtlh89epTHH3%2Bc1NTUCsqsdH%2BU4y43nopgqRSGDh1q/ffhw4f5xz/%2BQXR0NE2bNsXNzY0ff/yR5cuX89RTT1VgliWFhITw9ttvY7FYsFgs7NixAzc3N%2Bt6k8lE1apVmTp1agVmeXXOnH9WVhaxsbHWz%2BPi4kps4%2BXlddN9z4BzH/dt27YxfPhw6x%2BtvXv3tllf3KGMjIy84bldjzPnvnLlShYuXAgU5fnII49QpYrtm8Vnz56lfv36FZHeNTnzcZeKZbKU9p6HyB9Yr169eOaZZ%2BjatavN8k2bNvG3v/2NtWvXVlBm1xYfH09CQgI%2BPj4Vncrv4sz5R0REkJSUVKIz5gyc8bj/%2BuuvmM1mOnfuzMqVK22Ou8lkwsvLq0Sn72bhrLlfvnyZTz/9FLPZzLhx4xg3bhzVqlWzri/O/e677y7xjsjNwFmPu1QsFcFS6YSFhZGUlFSio7Fv3z769u3Lzp07Kygz%2B2RnZ1NQUFBiZq9OnToVlFHZOHv%2BzkrHXey1bds2WrRogaur3iyWPzYVwVLpPP3001gsFqZNm0ZQUBAAR44cYdy4cdSo8f/t3XlczWnfB/DPoUWWIllikiNDllLShKRUhorhZszYIsZ047Y/tkIzLTKWmYwsZaiZstyG0cgsyjJCqMg%2BMipNpm7GCC0a5XSeP7w6r07ndM7J6Fy/6/R9//M8XXW/Xp/7enr0Pdfv%2Bn2/rREZGck4oXKpqalYs2YN/ve//wF49YhPJBLJ/uft27cZJ1SN5/wFBQXYvHkzbty4obSQFNI98tp43vfi4mLExMTUue9CftGJ5%2BwAcOHCBdy4cQOVlZUK2ZVdDRIK3vedaBd9zCONTnh4OBYsWABXV1e0bt0aUqkUz549w6BBgxAWFsY6Xp1CQkJga2uLHTt2cPVouxrP%2BZcvX44nT55gypQp3GXnfd9v3LiB0aNHU3Yt%2BuyzzxAXFwdra2u0aNFC7nuavmDMCs/7TrSPToJJo1BYWAhzc3OIRCIUFhYCeNUyJyUlBSKRCG5ubmjWrBkA4T4e7tevH3744QdYWFiwjvJaeM5vY2ODhIQEdO/enXWUeuN5321tbbFnzx7Y2tqyjlJvPGd3dHTEmjVruHyRjOd9J9pHJ8GkUXB3d0dqairatm0r1y6t%2BjPgxo0bBf94eMCAAbh8%2BTKXxQzAd/6uXbuiqKiIdYzXwvO%2Bd%2BjQQaFDAS94zt60aVNui0ie951oH50Ek0ahoKAAnTp1gkgkUtsbtXPnzlpKVT9RUVGIjo6Gq6srLC0t5VpeAcK%2BpwfwnT8xMRFbt27FjBkzlGZ3dHRklEw9nvf9%2BPHjiI6OxoIFC5RmF%2BpTG4Dv7JGRkcjLy0NoaKhCf2mh43nfifZREUwIJ3x9fev8nkgkEvwLHzznt7a2rvN7Qn56AOjOvte8iyr0pzYA39l9fX1x5coVSKVStG3bVqGQFPKLoDzvO9E%2BKoIJIYQIEq9PbQC%2BsyckJKj8/r/%2B9S8tJak/nvedaB8VwYRw4vvvv1f5/bFjx2opyevhOX/1y5R1EfIjVp73nRBCGhIVwYRwwt3dXe5riUSCx48fQ09PD7a2toJ%2BrA3wnd/a2lrWWxdQbBMl5EesurDvdRHyvvOc3dfXV2V2%2Bp0huoK6QxDCiVOnTimslZWVISgoCD179mSQqH54zl/7DqREIkF%2Bfj4iIyMxd%2B5cRqk0w/O%2B1y62qvc9NjYWixYtYpRKMzxnd3Jykvv65cuXuH//PlJSUjBnzhxGqTTD874T7aOTYEI4l5eXh0mTJuHChQuso7wWnvNfv34dy5YtQ1JSEuso9cbzvqelpWHdunVqr3oIEc/ZDx8%2BjOTkZERFRbGOUm887ztpONRMjxDOZWVloaqqinWM18ZzfpFIhIcPH7KO8Vp43ndTU1Pk5uayjvFaeM7u6OjI5YcmgO99Jw2HrkMQwgll9/TKyspw584d%2BPn5sQlVDzzn37p1q8JaWVkZjh07BmdnZwaJNMfzvis7tSsrK8OhQ4dgZ2fHIJHmeM6u7EXQsrIy7N69W/DdFXjed6J9dB2CEE4oK8QMDAxgY2ODQYMGMUhUPzznr91rVyQSQV9fHzY2NpgxYwZMTEwYJVOP532v/VJfzX1ftGiRoAsynrPXfhG0mrm5OcLDwwX9e8PzvhPtoyKYEA6VlpZCIpEIuvhShff8vKJ9J5qo3Wu3upA0MzNT2XmBEN5QEUwIR7755hvs2rULf/31F4BX99wmTZok6NG3NfGc/9dff8Xu3buRm5sLiUQCsViMKVOm4J133mEdTS2e9/3PP//E3r17kZOTA4lEgm7dumHChAno2rUr62hq8ZwdAFJTU5GTk4OqqiqIxWIMHjxYYXqcEPG%2B70R76E4wIZzYtm0b9uzZg4ULF8Le3h5VVVXIzMzE1q1bYWBgAH9/f9YRVeI5//Hjx7F48WK8%2B%2B67GDduHCQSCa5evYqZM2di8%2BbN8PT0ZB2xTjzv%2B6VLl/Dxxx%2BjZ8%2BesLOzg0QiQUZGBvbs2YOYmBg4ODiwjlgnnrM/ePAAc%2BfOxb179yAWiyGRSPD777%2BjU6dOiI2NRYcOHVhHrBPP%2B04YkBJCuDB06FDpyZMnFdaPHz8udXNzY5CofnjO7%2BPjI42NjVVYj42Nlb733nvaD1QPPO/7%2BPHjpZs2bVJY37hxo/TDDz9kkEhzPGefPXu2dNasWdKnT5/K1oqKiqQfffSRdP78%2BQyTqcfzvhPtoxZphHCitLRU6eM8sViMoqIi7QeqJ57z379/H8OGDVNYHzZsGO7du8cgkeZ43ve7d%2B9i/PjxCuvvv/%2B%2B4Cd/8Zz94sWLWLZsmdzd8TZt2mDp0qVITU1lmEw9nvedaB8VwYRwwt7eHjExMXK9XSUSCWJiYmBra8swmWZ4zm9lZYUzZ84orKekpAj%2BbXOe971z5864fv26wvq1a9dgZmbGIJHmeM5uYmKCZ8%2BeKawXFxcL/k4wz/tOtI/uBBPCiYCAAEyZMgXnz59Hnz59AAA3b95ERUUFdu/ezTidejznnz9/PubPn49r166hX79%2BAICrV68iKSkJGzZsYJxONZ73fdasWfjkk0%2BQm5srK9ivXbuG%2BPh4LFmyhHE61XjO7uPjg9WrV%2BPTTz%2BFjY0NgFfZQ0JC4O3tzTidajzvO9E%2B6g5BCEeePHmCH374ATk5OTA0NIRYLMbo0aPRokUL1tE0wnP%2BCxcuYN%2B%2BfXLZ/fz8BH%2BaCvC974cPH8aePXsU9t3Ly4t1NLV4zV5RUYGgoCAkJibKegU3bdoUEyZMwIoVK9CsWTPGCVXjdd%2BJ9lERTAhHUlJS0KRJE7i4uAAA1q5dCxcXFwwdOpRxMs3wnP/x48coLi6GWCwGAPz0009wdHREu3btGCdTj%2Bd9f/nyJZ4%2BfSp7lH3lyhX06dMHBgYGjJOpx3N24NX1h7y8PBgYGKBLly5o3rw560ga4X3fifbQnWBCOBEfH4/FixfLer0CgJ6eHhYtWoRvv/2WYTLN8Jz/woULGD58OI4ePSpbi4uLg7e3Ny5fvswwmXo87/vt27fh4eGBmJgY2drSpUsxcuRI3L17l2Ey9XjO/vTpUyxcuBBxcXGwtbWFtbU1vLy8sHjxYpSUlLCOpxLP%2B04YYNqbghCisWHDhklPnTqlsH7ixAmph4cHg0T1w3P%2BMWPGSKOjoxXWo6KipOPGjWOQSHM87/vEiROloaGh0oqKCtmaRCKRhoaGSqdOncowmXo8Z1%2BwYIF0ypQp0uzsbNnar7/%2BKp06dap0%2BfLlDJOpx/O%2BE%2B2jk2BCOPHkyRN06dJFYV0sFsud8gkVz/nz8vIwcuRIhXUvLy9kZ2czSKQ5nvf99u3bmD59ulxHgiZNmmDatGm4efMmw2Tq8Zw9NTUVwcHBsLKykq316tULQUFB%2BOWXXxgmU4/nfSfaR0UwIZxwcHBAZGQkysvLZWsvXrxAVFQU7O3tGSbTDM/5u3Xrhp9//llh/dSpU0oLTCHhed/Nzc1x4cIFhfXMzEzBt7viOXuzZs3w4MEDhfWioiLo6Qm7qRTP%2B060T9i/zYQQmaCgIMycORNDhgyRDT/Iz8%2BHmZkZtm/fzjacBnjOv2jRIsydOxepqamyNmN37tzBpUuXEBkZyTidajzv%2B%2BzZs7Fq1SpcuXIFffv2BQBkZWUhMTERn3zyCeN0qvGcfdy4cQgMDMTixYtlv%2B9ZWVn48ssvMWbMGMbpVON534n2UXcIQjhSUVGBs2fPIi8vD3p6eujatSuGDBmCpk2byn7mwYMHaN%2B%2BPZo0Ed6DHp7z3717F9999x3u3bsHPT09WFpaYtKkSbCwsGAdTS2e9/3s2bP49ttv5fbd19cXAwYMYB1NLV6zSyQSbN68GQcPHsTTp08BvJoY5%2BvrC39/f8GfBvO670T7qAgmRMf0798fR44c4aI4U4bn/KNHj8bOnTthbm7OOkq98bzv/v7%2BCAsLQ/v27VlHqTehZy8qKoK%2Bvj5atWql8L2dO3di4sSJMDY2ZpDsnxH6vhPtENZHfkLIP8b751qe8//xxx94%2BfIl6xivhed9z8jIwIsXL1jHeC1Cz25qaqq0AAaAqKgopeOVeSD0fSfaQUUwIYQQQuqN5w9OhABUBBNCCCGEkEaIimBCCCGEENLoUBFMCCGEEEIaHSqCCdExIpGIdYR/hOf8lJ0QQvhBRTAhnPj77781%2BjkDAwOuCxqe8/P8ohDP%2B04IIa%2Bj6aeffvop6xCEEPX69%2B%2BPjIwMFBUVoVWrVjA1NVX6c7NmzRJs386SkhIcOnQIiYmJ6Nu3Ly5evIimTZvCxMRE9jNCzn/37l1kZmbC3NwcDx48gLGxsVzh6OzsjK5du8oNoRACXvd9%2BfLlePHiBdq1awcjI6M6f65bt27o1asX9PX1tZhOM6WlpcjOzoaJiQnKy8thYGAg930hZ1enqKgIzs7OKv9vw4ou7zt5c2hYBiGcuHfvHs6fP48LFy4gPT0dLVq0wJAhQ%2BDi4oLBgwejZcuWrCOq9Ntvv2H69OkwNzfHb7/9hp9//hnbt2/HsWPHEB0djXfeeYd1xDo9e/YMCxcuRHp6OgAgKSkJa9euxf3797Fz50507tyZccK68bzvERERSE1NRVZWFqytreHi4gIXFxfY2dkJbrJdbS9evEBoaCgOHz4M4NXvzPr161FeXo4vvvhC7gOIECUmJuLrr79Gfn4%2BEhISEBcXh3bt2sHf3591NJV433eiXcL%2BV4QQIiMWizFlyhRs3boVFy9exObNm/H8%2BXMsXLgQAwcOZB1PrbCwMEyaNAmHDx%2BWnb6sW7cOkydPxoYNGxinUy0sLAxGRka4ePEiDA0NAQDh4eHo2LEjwsLCGKdTjed9X7x4MQ4dOoTU1FT4%2B/vj6dOnWLlyJQYOHIhFixaxjqfSxo0bkZ2djYSEBNnvzPz58/HkyRPB/87s27cPGzZswLhx41BZWQkA6Nu3L3bv3o2tW7cyTqcaz/tOtI%2BKYEI4UVFRgczMTOzatQv/%2Bc9/4O/vj5SUFAwZMgTz589nHU%2BtGzduYOzYsQrrEydORHZ2NoNEmjt79iyWLFkid13A1NQUAQEByMjIYJhMPZ73vZpEIoFIJIKhoSFMTExQWlqKrKws1rFUSk5OxqpVq9CzZ0/ZWs%2BePREaGoozZ84wTKZefHw8wsLCMHXqVNmJ%2B5gxY7BhwwYcPHiQcTrVeN53on16rAMQQjTj4OAAiUSCIUOGwNXVFQsWLIC1tTU3LzOZmpri3r176NKli9x6ZmYm2rZtyyiV5pSNWC0qKoKenrD/GeV53wMCApCZmYmCggL07NkT/fv3x6xZszBgwADBZy8rK1N6V7aqqgoSiYRBIs0VFhbCyspKYd3CwgJPnz5lkEhzPO870T46CSaEE3PnzsWgQYNw%2BfJlHDhwAN9%2B%2By1%2B%2BuknPHjwgHU0jXz88cdYvXo19u7dC6lUiosXL2LLli0ICQnBjBkzWMdTadSoUVi7di3u3r0LkUiE58%2Bf4%2BLFi1izZg28vb1Zx1Oprn0PDg4W/L5fvnwZ%2Bfn5cHJywujRozF69Gh4enoKvgAGAHd3d0RERKC0tFS2dv/%2BfYSFhcHV1ZVhMvX69euH77//Xm5NKpUiJiYGtra2jFJphud9J9pHL8YRwhmJRIJbt27h0qVLyMzMxJkzZ2BmZoZTp06xjqbWqVOnsHv3buTk5EAikUAsFsPPz0/whWRFRQW%2B%2BOIL7N27F5WVlRCJRGjatCnef/99rFy5Es2aNWMdUSVe9x0A/vzzT1y6dAkZGRm4dOkSCgsLYWNjgwEDBmDevHms49WppKQEgYGBOHnyJKqqqmBsbIySkhIMGTIEGzZsQJs2bVhHrNNvv/0Gf39/tG3bFllZWRg0aBDy8vLw999/46uvvkKvXr1YR6wTz/tOtI%2BKYEI4k52djbS0NKSlpeHSpUto0qQJBg8eLPiXnF6%2BfFnn1YFbt26hT58%2BWk5Uf3///Tfu378PiUQCCwsLtGjRAs%2BfP0fz5s1ZR6tTRkYG7O3tFfa%2BoqICZ86cgaenJ6Nk9VNaWopLly7hxIkTOHLkCJo0aYJr166xjqXW/fv3kZOTg5cvX0IsFsPKygpSqVTw15hevHiBo0ePyn1weu%2B999CiRQvW0TSSn5%2BP3NxcuX0npDYqggnhxIIFC3D58mUUFxfDwcEBzs7OcHFxgbW1NetoGvnoo4%2Bwfft22RvbAFBcXIzPP/8chw4dwq1btximU23r1q1KTx1/%2BuknrF%2B/HikpKQxSaaZXr15ITU1V6Ct969YtTJo0CdevX2eUTL2UlBSkpaUhPT0dWVlZsLS0hLOzM5ydneHk5CToE/jDhw9j3LhxCuvXrl1DcHCwrIWXEFW3pKuttLQUX375JVatWsUglWY8PDzw3XffoXXr1nLrDx8%2BxNixY3HhwgVGyYgQCfuNDkKITIcOHRAeHi74P/51ef78OaZPn46vvvoKrVq1wqFDh/D555/D2NgY27dvZx1Ppf379%2BPZs2eyP/65ubkICQnBpUuXMG3aNMbpFO3btw8hISEQiUSQSqVwdnZW%2BnODBw/WcrL6Wb58OQYNGoQPP/wQLi4u6NixI%2BtIGgsNDUVxcTH8/PwAAE%2BePMGmTZtw%2BPBhjBgxgm04NaZNm4aoqCj0799ftnbkyBFs3LgRBgYGgiuCjx07JvsgWlBQgJCQELkP29XrQhtiQ9ijk2BCOFJSUoLExETk5eVhzpw5uHbtGqysrBTe/BeiiooKLF68GPn5%2BWjRogXu3r2LOXPmYPr06YKf2pSfn49Zs2ahX79%2B6NixI2JjYzF48GAEBgaia9eurOMplZGRgaqqKkyfPh2RkZFyQwJEIhGMjIzQo0cPhUlaQlJ9baC0tBT5%2Bfno3r07KioqBD8YBnh14jt79mx8%2BOGH6NixIyIiItChQwesXr1a0ANKgFct0iIiIrB582aYm5sjODgYN2/exKxZs/Dxxx8rFJisFRUVYePGjQCAhIQEeHl5KRwUNG/eHGPGjBH8i31Eu6gIJoQTPE/%2BqiaVShEaGor//ve/iIuLw4ABA1hH0lhRURH%2B/e9/49atW4iIiBD8aV61goICdOrUSfB3UJWpqKhASEgIt9O/8vLyMGvWLBQWFiIwMBCTJ08W/KS7aseOHUNgYCAqKirg6emJ5cuXo1OnTqxjqbV161Z89NFHghzlTISHimBCODFt2jQMGDAACxYsgL29PRITE2FhYYGNGzciLS0Nhw4dYh1Rga%2Bvr0LxJZVKcfXqVbRs2RJvv/22bD0uLk7b8VRSNhnr%2BfPniI%2BPR58%2BfTBkyBDZutC6FAQEBGDVqlVo2bIlAgICVP7sunXrtJSq/sLCwnDz5k0EBwdj4sSJSExMxPPnzxEQEAArKyvZ6Z9Q1G4rBgCPHj3Cli1bMGzYMLi7u8vWlQ0wYamwsFBhLSMjA0FBQViyZAmGDx8uWxdaMVzz5U91w2scHR21lIrwgO4EE8KJGzduKB37OXHiROzdu5dBIvWcnJzqtS4kaWlpStft7Ozkvs/jCSsvkpOTsW3bNqXTv2bOnMkwmXJbtmxRut6uXTvcvHkTN2/eBPDqd0ZoRbC7u7vS32WpVIp169bhs88%2Bk11PuX37NoOEdfP19UVqairatm0LX1/fOn9OiNkJW1QEE8IJHid/Ce2EtD7i4%2BNZR3htNU93rays4OPjA3Nzc4aJXg9v07946NVdl5MnT7KO8NpqjtBOSkqCpaUlwzSEJ1QEE8KJ6slfs2fPlk3%2BSkhIwDfffIPFixezjqcUz4/lv//%2Be3h7e8PAwEDpY%2B6ahHaqV1NUVBQ395drq57%2BtX79etmakKd/afpYXiQSCe4%2BfOfOnVlHeCMmT56M6Oho9O3bl3UUwgEqggnhxMSJE9G%2BfXvs3r0bzZo1w4YNGyAWixEaGsrF5C/ebNmyBa6urjAwMKjzMTcgzEfbNY0aNQo7duyAv78/OnXqJOhuELUFBQUhMDAQ77zzDqqqqjB%2B/HjZ9K81a9awjqeA58fyvXr1wrlz59C2bVtYW1urvOYjtOw1mZmZ4fHjx6xjEE7Qi3GEEKLD3N3dUVhYWGdRI%2BSCppqyqWtCVFBQINtnVX9aRSKR4F4uS09PR//%2B/aGnp4e0tDSVRbCQO9EEBAQgMTERNjY26Ny5s8KHPqE9cSJsURFMiIDVbPejrFtBTUK8f6suc01Cy6/uLfNqQny0XVN6errK7wutoCksLIS5uTlEIpHSjgU1Ca2QVHeCWhMPHz54xNu1K8IWXYcgRMDS0tIwbdo0GBkZ1dmtABBuhwJVmWsSYn5Vj7NrEuKj7Zqqi9y8vDzk5OSgqqoKYlroTG8AABafSURBVLEY3bt3Z5xMOXd3d9mVguqOBdVnNTVPWYW47zy/XFZXdwhlhPzfk4pcUh90EkwIJyoqKri6z/m6du7ciYkTJ8LY2Jh1FJ1QXFyMgIAAnDx5EiYmJpBIJCgrK4OjoyO2bduGVq1asY4op%2BZwjxkzZsDHxwdOTk5Kh0zoystcQpCQkKDxz/7rX/9qwCT/3IkTJ7Br1y7k5uZCIpFALBZj6tSpgr67T9igIpgQTjg4OMDT0xPe3t5wdnaGnp5uPsjp378/jhw5AgsLC9ZR5OTk5KB9%2B/Zo1aoVzp49i1OnTqF3796YMGEC62gqLV%2B%2BHDk5Odi4cSO6desGAMjOzsbKlSvRo0cPhIeHM05Yt//7v//D6dOnYWRkhBEjRsDb2xsODg6sYzU6L168wJ07dyAWiwX3oam2//73v1i/fj2mTp0Ke3t7VFVVITMzE/v370dgYKDg//%2BVaBcVwYRw4ty5c0hOTsbJkyfx8uVLeHp6wsfHBwMHDuRmFKsmak7DE4oDBw4gJCQEsbGxaNmyJT744AMMHDgQWVlZmDBhAhYuXMg6Yp0GDBiA2NhY2NjYyK1fv34dH3/8scZXVlipqKjAuXPncPz4cZw6dQpGRkbw8vKCt7e3wn8n8mZkZ2cjMDAQK1euRPfu3fHhhx/i3r17MDIywo4dOzBw4EDWEevk6emJefPmKZz6JiQkICoqCklJSYySESHSnb%2BchOi4IUOGICQkBGfPnsWWLVtgZGSEVatWwcXFBSEhIazj6bRdu3Zh/fr1eOedd/Ddd9%2BhV69e2LVrFyIiInDw4EHW8VQyNDRU%2BiFJJBIJcuBEbQYGBnB3d8e6detw9uxZTJ48GQcOHMAHH3zAOprOCg4OhoWFBbp27YpDhw6hpKQE586dw%2BzZs%2BV6NgvR48ePZVMda7K3t8f//vc/BomIkFERTAhnmjRpAgcHB7i4uMDNzQ3l5eU4c%2BYM61g67eHDh7LH8L/88gs8PT0BAB07dkRZWRnLaGq5u7sjODgY%2Bfn5srW8vDzBDpyoTSKR4Pz58/j000/h7u6Or7/%2BGqNHj8Y333zDOprOun79OhYtWgRTU1OcOHECw4cPh5mZGUaNGoXc3FzW8VTq1auX0uE2CQkJgn0ZlLCjm5cKCdFBL168wJkzZ5CUlISUlBQ0b94cI0eORGxsLPr168c6nk7r1q0bjh49ClNTUxQWFsLT0xOVlZWIiYmBtbU163gqLVu2DP/5z38wYsQI2cuGxcXFcHFxEeTAiZpWrlyJX375BVKpFB4eHli3bh0GDx6Mpk2bso6m01q1aoW//voLenp6uHr1Kv79738DeNXWTagj2qstW7YMfn5%2BSEtLk/27ePXqVWRlZSEqKopxOiI0VAQTwgknJyc0b94cw4cPx7Zt2%2BDo6CjI1mK6aMWKFVi0aBGePXuGyZMnw8rKCiEhITh%2B/Ljg/7AaGxsjPj4eWVlZyM3NhaGhIcRisewlOSGrqKjA2rVrMXTo0EbRGUUoxo0bhzlz5sDAwABvvfUWhgwZgv3792PDhg2Cvv8OvLr2cPjwYXz77bfIycmBoaEhHB0dERERAXNzc9bxiMDQi3GEcOLs2bManYLx3mJMiC/GAUBVVRVKSkpgYmICAPjrr79gYmICfX19xslUmzlzJnx8fDB8%2BHBufyeI9h0/fhwFBQUYNWoUzMzMkJKSgqqqKgwbNox1NJWOHj0KDw8PNG/enHUUwgEqggnRMUJtMaap8PBwzJkzB23atGGaIyMjA/b29tDT01M7Pc7R0VFLqerviy%2B%2BQHJyMgoKCuDs7Axvb294eHigRYsWrKMR8sa5urri2bNnGDp0KEaNGgVXV1cYGhqyjkUEiopgQnSMUE9SASAxMRFff/018vPzkZCQgLi4OLRr1w7%2B/v6soymwtraWTS5Tde9XiJPLlLlz5w6Sk5ORnJyM/Px8uLq6wtvbGyNHjmQdjQiAh4cHDh06hDZt2qidHifkiXEAcOXKFdnv%2BpMnT%2BDu7g5vb2%2B4uLgI/skN0S4qggnRMUItgvft24ft27dj9uzZ2LhxI3744QdkZmYiPDwcvr6%2BmDdvHuuIjUJJSQn279%2BPqKgolJeXc1HAk4aXkJAAHx8fGBgYqJweJxKJuJq8duvWLSQlJWHv3r3Q09MTfF9sol30YhwhRCvi4%2BMRFhYGNzc3fP755wCAMWPGoHXr1ggKChJcEVxYWKjxz3bq1KkBk/xzRUVFOHnyJJKTk3Hx4kV0794ds2fPho%2BPD%2BtoRCAOHz6s8ehkHorg58%2Bf4/Tp00hOTsa5c%2BfQoUMHeHt7s45FBIaKYEKIVhQWFsLKykph3cLCAk%2BfPmWQSLW6HglXPzyr%2BT0hn6b6%2BvoiMzMTlpaW8Pb2RkBAABedIYh2OTk5yf73J0%2Be4MCBA/D09ISNjQ309fVx%2B/Zt/PTTT5gyZQrDlOolJCQgOTkZ58%2Bfh5mZGby9vbFnzx7BtzIkbFARTAjRin79%2BuH777/H/PnzZWtSqRQxMTGwtbVlmEy5mvceT58%2Bjfj4eAQEBMDGxgYGBga4desWPvvsM8FPLrOzs8OqVauoCCAq1XwSM336dAQGBmLy5MlyP%2BPo6IgDBw5oO1q9REREYOTIkYiLi6P%2B6UQtuhNMiI4R6p3g3377Df7%2B/mjbti2ysrIwaNAg5OXl4e%2B//8ZXX32FXr16sY5YJzc3N3z55ZcKf1Rv3LiBOXPm4Ny5c4ySaebly5d4/PixbEyyVCpFRUUFbt%2B%2BTY%2BIiQI7OzskJCRALBbLrefk5GD8%2BPG4evUqo2TqSaXSOl/qq6yspBfjiBw6CSZEx0yYMAEtW7ZkHUNBjx49kJSUhMTEROTm5kIikcDDwwPvvfee4Nt1lZWV4eXLlwrrpaWlqKysZJBIcydPnsTq1auVXjlp164dFcFEQe/evbFz5058%2BumnsvZipaWl2LJlC%2Bzs7BinU%2B3x48eIjo5Gdna23Ie%2ByspK5OTkqG13SBoXOgkmhCM8tRiry/Pnz5Gfnw99fX289dZbXPTwDA0NxenTp7Fo0SJYW1tDKpXixo0b2LJlC8aOHYvFixezjlgnLy8vODo6ws/PD5MmTcLOnTvx9OlThIaGYu7cuRg3bhzriERgcnJy4O/vj2fPnsHS0hJSqRR5eXno1KkToqOj0blzZ9YR6%2BTv74/8/Hy8%2B%2B67iImJwYwZM5Cfn4/jx49j5cqVmDZtGuuIREDoJJgQTtRuMQYAffv2RXh4OCoqKgTXXaG2kpIShIeH4%2BjRo5BIJJBKpTAwMMDEiROxdOlSQY/FDQgIQIsWLbBu3ToUFRUBAMzMzDBlyhTMnj2bcTrV7t%2B/j%2BjoaHTp0gV9%2B/bFo0eP4OnpiSZNmmDDhg1UBBMFVlZW%2BPnnn3H%2B/Hnk5OQAAN5%2B%2B20MHjwYenrCLhsyMjIQExMDe3t7pKamws3NDQ4ODti5cyfOnDlDRTCRI%2BzfZkKIDG8txmpbs2YN7t27h9jYWPTu3RtSqRTXrl3D2rVrUV5ejtDQUNYR66Snp4clS5ZgyZIlsiLY1NRU4eeEOLLa2NgY5eXlAACxWIysrCx4enqiW7du%2BOOPPxinI0JlYGAANzc3uLm5sY5SL1KpFB06dAAAdO/eHb/%2B%2BiscHBzg5eWF3bt3M05HhKYJ6wCEEM3w1mKsttOnT2PdunVwdHREixYt0LJlSzg7OyM8PBw//vgj63gaMzU1VVoAA0BUVBSePXum5USqubq6Ijg4GNnZ2XBycsKRI0dw69YtHDhwAO3bt2cdj5A3qnfv3jhy5AgAoFevXkhNTQUA%2BsBHlKIimBBOVLcYq0nILcZq69Chg%2BwUtaby8nK0bt2aQaI3T4ivWKxatQqWlpa4efMmPD090a9fP7z//vvYu3cvVqxYwToeIf9YamoqKioqAABLly5FTEwMvv76a4wZMwY3b97E6NGjMW/ePHh5eTFOSoSGXowjhBM8thir%2BSZ2Wloa9u/fj3nz5sHGxgZNmzbFnTt3sHnzZvj5%2BcHPz49d0DdEKO3pAgICsHz5crRp0wYZGRmws7OTaw1VWloKQ0NDahdFdIK9vT1%2B/vlndOzYER4eHtizZw/09fVhZmaGhw8f4sSJE2jdujW8vb3rbJ9GGicqggnhyIsXL%2BRajInFYkG3GNN0QINIJBL01DVNCaUItrW1RWJiIrp27Sp7JFzXFQ5CeOfq6oqhQ4eif//%2BCAgIwOrVq%2BtsE8nDyGeiPVQEE8IZHluMNRZCKYKnTp2K/Px8WFpaIiMjA/b29nWe%2BsbFxWk5HSFv1smTJxEZGYmSkhIUFhaiQ4cOaNJE8banSCSSmwRJCHWHIIQTPLcYA1692KdKp06dtJRE90VGRiIxMRElJSWy6xBCfVpAyD/l4eEBDw8PAIC7uzu%2B%2B%2B47tGnThnEqwgMqggnhBM8txoBXf5xEIpHs5bHad/N04TqEULRp0wbTp0%2BXff3RRx/ByMhI5X9GiO3dCKmvU6dOsY5AOELXIQjhhJ2dHfbt24fevXvLrV%2B9ehUzZ85EZmYmo2SaKSgokPtaIpEgPz8fkZGRmDt3LlxdXRkle3PCw8MxZ84cLk%2Bh%2BvfvjyNHjjC/ykEIIdpCJ8GEcIL3FmPKRq126dIFxsbGWLZsmeCLYE1GVgcGBjJM%2BM/QeQghpLGhIpgQAavZYmz06NFYsWJFnS3GeCUSifDw4UPWMVTifWQ1IYQQRXQdghAB06UWY1u3blVYKysrw7Fjx9C7d29s27aNQSrNeHl5YcWKFXBzc5PrAJGSkoKgoCCkpKSwjviPCaWzBSGEaAudBBMiYFlZWawjvDFpaWlyX4tEIujr62PMmDGYMWMGo1Sa4X1kNSGEEEVUBBPCCd5bjK1fvx4dO3ZU6N8pkUiQlZUFExMTRsnUqx5ZPX/%2BfNkaTyOrCSGEKKLrEIRwwtramusWY3VNLvv999/x3nvv4dq1a4ySqcfjyOr6ousQhJDGhk6CCeFE7UlHtVuMCdHBgwcRFRUF4NXJ6fjx4xVOgouLi5VeNRCSHj16ICkpSW5ktYeHh6BHVtfXhAkT6hw1SwghuohOggnh3PXr17Fs2TIkJSWxjqKgsrISP/74I6qqqhAYGIjAwEC0atVK9n2RSAQjIyMMHDhQ0NchqvE6slqT9m6EENLY0EkwIZwTcosxfX19jB07FgDw1ltvoX///tDT4%2B%2BfHZ5HVlN7N0IIUY6/v0aENFKqWow5OzszSFQ/6enpSE9Pr/P7Qi7GeB5ZHR8fj7CwMLi5ueHzzz8HAIwZMwatW7dGUFCQoPedEEIaEhXBhHCC5xZjgGJ%2BiUSCP/74A8XFxRgxYgSjVJo5ffq0wshqZ2dnhIeHY%2BbMmYIugqm9GyGEKEdFMCGc4LnFGPDqRFKZ8PBwhU4XQsPzyGpq70YIIcrRi3GEcILnFmOq3L9/H%2BPGjZMbES0ENfOkpaVh//79dY6sFvLY6sbQ3o0QQl4HFcGECFjNFmMFBQUwNzdX2mLMwsIChw8fZhHxH9uzZw%2BioqJw7tw51lHk6NLI6hcvXsi1dxOLxTrV3o0QQl4HXYcgRMDGjh0LfX19WYuxGTNm1NliTOjc3d0Vrj2UlZXh2bNnWLFiBaNUddOlkdWGhobw8fHhsr0bIYQ0FDoJJoQT6enp3LYYA4CEhAS5r6tf7Ovbty8sLS0ZpdIMzyOreW7vRgghDYmKYEI4oaxFWk28tLoqLy/H77//jqqqKnTp0oWLKWU8j6xetGgR7t27h9WrVyu0d3NwcBB0ZwtCCGlIVAQTwglfX1%2B5r2u3GFu/fj2jZJqprKzExo0bsW/fPtmJpJ6eHkaPHo3g4GBBn0gWFBTIfV17ZLWrqyujZOrZ2dkptHcDgKtXr2LmzJnIzMxklIwQQtji87kqIY0Qzy3GgFct3lJSUrBjxw7Y29ujqqoKV65cQVhYGCIiIgR5L7ha586dFda6dOkCY2NjLFu2TNBFMM/t3QghpCHRSTAhnBNqi7HaBg4ciC%2B//BJOTk5y6xcvXsTSpUsF1x1CEzdu3ICvry%2BuXr3KOoocXWnvRgghDYlOggnhXEpKChdv%2BkulUrRt21Zh3dTUFGVlZQwSaY63kdW1r84AQHBwsMLa%2BvXrqQgmhDRaVAQTwgneWozVNnDgQGzatAmbNm2SvQxXXFyML774QuF0WGh4G1mtS%2B3dCCGkodB1CEI4wXOLMQB4%2BPAhpk2bhj///BNisRgAcO/ePVhYWGDHjh1K790KRWFhocqR1X369GGUTD2e27sRQkhDoiKYEM7w2GIMAFasWIERI0ZAKpUiNzcXhoaGEIvFcHZ2ViguhYbnkdU8t3cjhJCGRNchCOEEzy3GAKBly5b45JNPUFlZiXfffRfe3t5wcnISbGeLmiOrpVIpxo8fr3RktZWVFYt4Gjt58qTc17XbuxFCSGNFJ8GEcCIsLAwpKSkICgpSaDHm6enJxb1gqVSKjIwMHDt2DMnJyQAALy8v%2BPj4wM7OjnE6eZWVlfjxxx9lI6sDAwPrHFltYmLCMOnruX79OpYtW4akpCTWUQghhAkqggnhhK61GCstLcWuXbsQGxuLiooKdOrUCR988AH8/PwE1%2B2C95HVygi1vRshhGiL7vyLToiO47nFWLWysjL88ssvOHbsGM6dO4cOHTpgxowZ8Pb2xqNHj7Bp0yakp6dj9%2B7drKPKSU9PR3p6ep3fF/LIat7auxFCiLZQEUwIJ3huMQYAc%2BbMwfnz52FsbAwvLy/ExcXB1tZW9v0ePXqguLgYq1atYphSudot0mqPrBYy3tq7EUKIttB1CEI4wXOLMQBYs2YNfHx8VL4M9%2BjRIy5eNqtWPbI6ICCAdZQ68dzejRBCGhIVwYRwgucWY7qKh5HVPLd3I4SQhkTXIQjhBG8txhoDoY6s1pX2boQQ0pDoJJgQjvDUYkyXqBtZ7efnxyZYHXS9vRshhLwJVAQTwimeWozxjueR1brY3o0QQt4EKoIJ4YiyFmNeXl5yLcZMTU0F12JMV/A4slpZi7SahNzejRBCGhIdDRDCCZ5bjPGO55HVPLd3I4SQhkRFMCGcMDMzQ3R0tMqX4QYMGICDBw9qOZnuW79%2BPVJSUrBjxw6FkdURERGCHlkdHx%2BvdL26vRshhDRWdB2CEELU0LWR1QAf7d0IIaQhUXNRQghRQxdGVtcm1PZuhBCiLXQdghBC1OB5ZLW69m6EENJY0XUIQghRg%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\" class=\"center-img\">\n",
       "    <img 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2BHu7o6TJ08atb9cJ5iIiIiIkJ%2BfX%2BVx7y4uLnB1da3V%2BcOHD6/VcQUFBXj%2B%2Bef1tjk7O0tTKPLz86u06ezsLD0y3liYBJNpEPXkKG9v4OxZoHlzIDtbSBOnM8TMODI313U/Oxv488m4RufkJCauXK6LXVgIVHo6sFEpBP12k8sBe3uguFhc39XqW0LiyuUyODnZorDwDsrLxfxcTpjQQEhcNzcgIQGIjAREPvzuv/8VE9fCAigrExO7glzgZ7sKBaBWi4sv6t8SIH7sLS3FxX4gAf8vbl6%2BHAkJCXrbIiMjMWnSJKO2U1JSovfgH0D3kKKyP/%2BiSktLH7jfWJgEU/3m4ACYmeleTYxcrvsdKPI/PlEq913kf34iyGR/fZkauVwGmUwGuVwmLAkWxdZW90/V1raue/LoKv/MmOpdOKb48w78Pcb%2BSRoyZAhCQ0P1trm4uBi9HUtLyyoJbVlZGaysrB6439ra2qj9YBJMREREZGoEVEBcXV1rPfXBEG5ubrh27ZretmvXrklt17Tf2Am5CdaQiIiIiMhUBQQEIDU1FRULlGm1Wpw4cQIBAQHS/uPHj0vHX7lyBVeuXJH2GwuTYCIiIiJTI5cb/0uggoIClJaWAgB69uyJ4uJiLFiwAOfOncOCBQtQUlKCXr16AQCGDRuGr7/%2BGlu3bkVGRgZmzJiBrl27GnV5NIBJMBEREZHpMbEkOCQkBN9%2B%2By0AwM7ODqtXr8bx48cxYMAAnDx5Ev/6179gY2MDAFAqlfjwww/x6aefYtiwYWjYsCHi4%2BON3ifOCSYiIiIiozp9%2BvQDv2/Tpg2%2B%2BuqrGs8fMGAABgwYIKRvFZgEExEREZkaU1wa6CnDESQiIiKieoeVYCIiIiJTw0qwwZgEExEREZkaJsEG4wgSERERUb3DSjARERGRqWEl2GAcQSIiIiKqd1gJJiIiIjI1rAQbjEkwERERkalhEmwwjiARERER1TusBBMRERGZGlaCDcYRJINdvHgRPj4%2BuHjxYpV927dvR2hoaB30ioiIiKhmrASTwZ599lkcOnQITk5Odd0VIiKi%2BoGVYIMxCSaDmZmZwcXFpa67QUREVH8wCTYYR5AMVnk6RF5eHt544w20bdsW/fv3x4ULF%2Bq6e0RERERVsBJMRjV58mTY2Nhg69atOHv2LGbPng1HR8dHipGfn4%2BCggK9bS7e3nB1cDBmV3V8ffVfBbC0FBPXwkL/VQSFoN8QZmb6ryLbMLaK4ovYIoyY4GZmcr1XEZo2FRP3uef0X0WRycTFFBH770LU2Igee61WTNxaYSXYYEyCyWhKSkqQmpqK/fv3w93dHc2bN8dvv/2G77777pHibN68GQkJCXrbIidPxqTJk43ZXX2bNgkL7SUsso67u%2BAGBLK3r%2BsePD47O5HRbUUGh729tbDYH38sLDQAYOpUsfFFMjev6x4YxpT7L6rvKpWYuPRkMAkmozlw4AAcHBzgXikr8/f3f%2BQkeMiQIVVWlHDp2xdYt84o/dTj66tLgIcPBzIyjB8fQM72E0LiWljoEuDLl4GyMiFNoGFDMXHNzHQJcHExoNGIa0MEuVyXAN%2B%2BDZSXi2lDo7kjJK6ZmRz29tYoLi6BRiOm8wsWiEngn3tOlwAvWwZcuiSkCQDAwoXGjymT6ZKwe/fEVg5FVpor%2Bi%2BKqHF5UmNfJ1gJNhiTYDIq7X2/Zcwf4%2B23q6srXF1d9TdmZxvSrYfLyABSU4WEFl0pKCsT14ZaLSZuBY1GfBuilJeLS%2BDVakHZ9Z80mnJhbWRlCQkruXRJbBsiEyWt1nSTYNFEJ6iix75OMAk2GEeQjKZbt264efMmzp8/L207depUHfaIiIiIqHpMgsloLC0t0bFjR0RHRyMjIwMpKSn44osv6rpbREREfz9yufG/6pn6d8Uk1LJly%2BDo6IihQ4fik08%2BQXh4eF13iYiIiKgKzgkmg3l4eOD06dPS91VWdoiMfNJdIiIi%2Bnurh5VbY2MSTERERGRqmAQbjCNIRERERPUOK8FEREREpoaVYINxBImIiIio3mElmIiIiMjUsBJsMCbBRERERKaGSbDBOIJEREREVO%2BwEkxERERkalgJNhiTYCIiIiJTwyTYYBxBIiIiIqp3WAkmIiIiMjWsBBuMI0hERERE9Q4rwURERESmhpVggzEJJiIiIjI1TIINxhEkIiIionqHlWAiIiIiU8NKsMFkWq1WW9edIHqY06fFxLW0BLy8gJwcQKUS04aPr0xMYKUSOHECCAwEUlPFtSGCry%2BwaRMwfDiQkSGmjS5dxMT18ACmTQOWLgUuXhTSREH0h0LiKhRyODra4saNO1Cry4W04XL6pJC4sLXV/TympgJ37ohpAwCCg8XENTcH7t0TE7uCWi0mrkwGWFkBpaWAqJRBVEInkwEWFkBZmbi%2BW1qKifswIn5WjxwxfsynGCvBRERERKaGlWCDMQkmIiIiMjVPYRKsUqnwwQcfYPfu3bCyssLYsWMxduzYKseFh4fj6NGjVbYPGDAA8fHxuHnzJtq3b6%2B3z8HBAUeMXKlmEkxEREREBvvoo4/w22%2B/Yd26dbh8%2BTJmzpwJd3d39OzZU%2B%2B4FStW4F6l6UEnT57ElClTMHz4cADAuXPn4ODggG%2B%2B%2BUY6Ri4g6WcSTERERGRqnrJK8N27d7F161asWbMGfn5%2B8PPzw9mzZ7Fx48YqSbCDg4P0Z41Gg2XLluGNN96Av78/ACArKwve3t5wcXER2uenawSJiIiIyORkZGRArVZDWemG6nbt2uHkyZMoL6/5Rtzt27fj5s2bePPNN6Vt586dg5eXl8juAmAlmIiIiMj0CKgE5%2Bfno6CgQG%2Bbi4sLXF1dH3puQUEBHB0dYWFhIW1r1KgRVCoVioqK4OTkVOUcrVaLzz77DKNGjYKtra20PTMzE2q1GgMHDkReXh6CgoIQFRVVq348CibBRERERKZGQBK8efNmJCQk6G2LjIzEpEmTHnpuSUmJXgIMQPq%2BrKys2nOOHDmCq1evYvDgwXrbs7Ky4OTkhKioKGi1WixbtgxvvfUWtm7dCjMzs0e5pAdiEkxEREREGDJkCEJDQ/W21XZerqWlZZVkt%2BJ7Kyuras/ZtWsX/vGPf%2BjNEQaAnTt3QiaTSectX74cISEhOHnyJAIDA2vVn9pgEkxERERkagRUgl1dXR97yoGbmxtu3LgBtVoNhUKXXhYUFMDKygr29vbVnvPDDz8gMjKyynZra2u9752dneHg4IC8vLzH6ltNeGMcERERERmkZcuWUCgUSEtLk7YdP34c/v7%2B1S5vVlhYiNzcXLRr105v%2B%2B3bt/HCCy/g559/lrbl5eXhxo0baNq0qVH7zCSYiIiIyNTI5cb/MoC1tTXCwsIwb948pKenIyUlBWvXrsWoUaMA6KrCpaWl0vFnz56FpaUlPDw89OLY2dmhXbt2iI%2BPR3p6On7//XdMnToVnTt3ho%2BPj0F9vB%2BTYCIiIiJT85QlwQAQFRUFPz8/jB49Gh988AEmTZqE7t27AwBCQkLw7bffSsdev34d9vb2kMlkVeIsXrwYrVq1wvjx4xEeHo7nnnsOH3/8scH9ux/nBBMRERGRwaytrbF48WIsXry4yr7Tp0/rfd%2B7d2/07t272jgNGzZEfHy8kD5WxiSYiIiIyNQ8ZU%2BMM0UcQSIiIiKqd1gJJiIiIjI1rAQbjEkwERERkalhEmwwjiARERER1TusBBMRERGZGlaCDfa3GcGysjJs2bKl1sf7%2BPjgyJEjAntUvfDwcKxYseKJt1sXcnNzceDAgbruBhEREVEVf5skeOfOnVi1alVdd%2BOhVqxYgbFjx9Z1N56I6OhopKen13U3iIiI/n6ewodlmJq/zXQIrVZb112oFQcHh7ruAhEREZm6epi0GtsTH8GYmBi89dZbetvi4uIwffp0XL16FZMnT0b79u0RHByM%2BfPno6ysDACwfft2hIaG6p1XMbXgyJEjiIqKwqVLl%2BDj44OLFy9WmXZw8eJFaV%2BFY8eOoXv37ggICMDkyZNx8%2BZNad%2BZM2cQHh6ONm3aoEePHti4cWOtru/gwYMICAhASUmJtO3QoUMIDAxEaWlplX59%2BeWXCA0NhVKpRHh4uPRElfnz5%2BPdd9%2BVjktMTETr1q2hUqkAANnZ2fD398fdu3cf2qfz589j3LhxUCqV6Nq1K9avXy/ty8zMxLhx4xAYGIjOnTsjISEB5eXlAHRV6/DwcL1YoaGh2L59OwDd%2BCcmJmLcuHHSOP3www8AgFmzZuHo0aNISEioEoOIiIiorj3xSvCrr76K8ePH4/bt27Czs0N5eTl27dqFefPmYfTo0fD09MSGDRtQWFiIOXPmANAlzg%2BiVCoRHR2NtWvXIikpCU5OTrXqy8aNG7Fo0SI4OzsjOjoa8fHxWLRoEUpLS/Hmm2%2Bif//%2BiIuLQ1ZWFubMmQNbW1uEhYU9MOaLL74Ia2trHDx4ED169AAA7N69G6GhobCystI7dt%2B%2BfUhISEBcXBy8vb2xY8cOjBo1Crt370bnzp0xa9YsaLVayGQyHDt2DGq1Gr/%2B%2BiuCgoLw008/oV27drCxsXlgf1QqFcaOHQs/Pz9s2bIFubm5mDZtGho3boyAgAAMHz4coaGh2Lp1K7KzsxETEwM7OzuMGTOmVmO4atUqzJ07F3PnzsXSpUsxZ84c7Nu3D7Nnz0ZOTg6USiUmTJhQq1gV8vPzUVBQoLetpMQFjRq5PlKc2rCw0H8VQqkUE9fXV/9VZBvG5uWl/yqCh4eYuK6u%2Bq8CKBRi6hNmZnK9VyFsbcXEtbbWf6WqZDKxcUXFFxlbdN/r8lNoVoIN9sST4ODgYDRs2BD79u1Dv3798Msvv%2BDevXsAgLy8PGzZsgUNGzYEAMTGxmLixImYOnXqA2NaWFigQYMGMDMzg4uLS637EhkZiS5dugDQJdoRERGIiYlBcnIynJ2dMWXKFACAl5cXLl26hPXr1z80CVYoFOjevTt2796NHj16QKPRICUlBfPnz69y7GeffYYJEyagW7duAIApU6bg4MGD%2BN///oeBAwfi1q1bOHv2LJo2bYq0tDSEhITgxIkTUhLcuXPnh17joUOHUFhYiIULF8LOzg7NmzdHTEwM5HI5vvnmG1hbWyMuLg4KhQLNmjVDQUEBPv3001onwV26dMGAAQMAABMnTsRrr72GgoICuLm5wdzcHDY2No88BWTz5s1ISEjQ2/bOO5F4991JjxTnUbi7CwsNnDghMDiATZvExhdp4cK67sHjE/gJh6OwyDr29gITSUdBb/oqiHzTJ5q5uWnHt7QUG18kUWPz56ezZJqeeBIsl8vRq1cvfPfdd%2BjXrx%2BSk5PxyiuvICsrC15eXlICDACBgYFQq9W4cOGCkL74%2B/tLf27VqpXUVlZWFjIyMqCsVMHTaDQwMzOrVdxXX30Vb7/9NsrKypCamop79%2B4hJCSkynGZmZlYsmQJPvnkE2mbSqVCTk4OrK2t0a5dOxw9ehSlpaV47rnn0KVLF/z444/QaDQ4evQoJk%2Be/NC%2BZGdnw9vbG3Z2dtK2119/HQAwd%2B5c%2BPn5QaH468dAqVSioKAAxcXFtbpWr0qVvIo21Gp1rc6tyZAhQ6pMfSkpcUFOjkFhq2VhoUuAL18G/px5Y3ReAwLFBPb11SXAw4cDGRni2hDBy0uXAEdHQ8hfLAC0aycmrqurLgHesAHIzxfSxI2xbz38oMdgZiaHvb01iotLoNGUC2nDMeeMkLiwttb9PGZkAJWmmxld69Zi4pqbA38WfITRaMTElcl0CbBKJa7yKaqqKZP9NfYmcu9QrbESbLA6uTGuT58%2BCA8Px%2B3bt7Fnzx4sWbIEZ85U/cWp%2BfMftEajgayajzIeJdnSVPPLoXJSW3Fjnbm5OdRqNTp27IjY2Nhax6/shRdegI2NDX766Sf88MMPePnll2FRzeftGo0G0dHR6Nixo972imSyU6dOOHr0KFQqFQIDA9GuXTskJCTg119/hY2NDVq0aPHQvlROcO9nWc27%2Bor5wLUdc/Nq3l0bepOiq6srXO/7qPn0abFvuMvKBMZPTRUU%2BE8ZGeLbECUnR1wC7%2BYmJm6F/Hyg0j0GxqRWi0lQK2g05eLauHNHTNwKJSXi2zBVopM8rVZcG6bc97rCJNhgdTKCAQEBcHNzw5o1a6DVatG%2BfXt4e3sjJycHRUVF0nFpaWlQKBRo0qQJzM3NcafSLz6tVqt3k9v9CZuFhYXe8bm5uVX6UTnxTk9Ph7m5OTw8PODt7Y3s7Gx4eHjA09MTnp6eSEtLw4YNG2p1fXK5HD2occoNAAAgAElEQVR79sT333%2BPvXv34tVXX632OG9vb1y9elVqw9PTE6tWrUJaWhoAoHPnzjh27BiOHz%2BOoKAg%2BPr6Qq1WY/369dVWlqvj5eWF8%2BfP692ot3jxYsyfPx/e3t74/fffpekoAJCamgonJyc4ODhUGfM7d%2B6gsLCwVu0SERERPc3q7G1E79698fnnn6Nnz54wMzNDp06d0LhxY8yYMQOnT5/Gzz//jLi4OPTp0wf29vZo3bo1ioqKsGHDBuTm5iI%2BPl5vNQdra2vcvHkTOTk5UKvVaN26NZKTk5Geno709HQsX768Sh%2BWLVuGw4cPIy0tDfPnz8fQoUNhbW2Nfv36obS0FLGxscjMzMSBAwewYMECODs71/r6Xn31VXz99ddQqVTo0KFDtcdERERg3bp12LFjBy5cuIAlS5YgOTkZzZo1AwD4%2BvpCLpfj4MGDaNeuHeRyOZRKJb799ttazQcGgJCQEDRq1Ei6lr179%2BLLL79ESEgI%2Bvbti7KyMmlfSkoKVqxYgWHDhkEmk8Hf3x8ZGRlITk5GdnY2YmNjIX%2BEd542NjbIycnB9evXa30OERER1QLXCTZYnSbBKpUKvXv3BqCbmrBy5UoAwODBg/Hee%2B/hpZdewocffghAV9GcOXMmEhMTERYWBq1WK62%2BAAAdOnSAp6cn%2Bvbti1OnTiEiIgKtWrXCyJEjMW3aNLz99ttV%2BhAREYHZs2cjIiICSqUS77//PgDddIQ1a9YgJycHYWFhiImJwYgRIx5plYO2bdvC0dER3bt3r3FKQu/evTF16lQsX74cffr0weHDh5GYmCjNs5XJZHjxxRfRqFEjuP9551ZQUBDkcjlefPHFWvVDoVBg5cqVyM/PR//%2B/bFgwQLMmDEDXbt2hZ2dHT777DNcuHABYWFhiIuLw%2BjRoxEZGQkA6NixI8aMGYPY2FgMHToUzZs3R0BAQK3HYNCgQfjhhx/wxhtv1PocIiIioidBpq2jp0z8%2BOOPmDNnDvbu3Vvt3FOiyv5cPtnoLC1192jl5IibE%2BzjK%2BjnW6nUrTwRGChuTrDI5d1E39T358ovRufhAUybBixdKmxOcEH0h0LiKhRyODra4saNO8LmBLucPikkLmxtdT%2BPqali5wQHB4uJ%2ByRujDPwpuQayWSAlRVQWmqaN8ZZWOhu/BDV97paNUPECjW1nPb5d/HEb4zLz8/H8ePHsXr1agwcOJAJMBEREdGjqofTF4ztiSfBt27dQnR0NNq2bYuIiIgn3bzBFixYgKSkpBr3T5gwocoT8UQKDg6WnqpXnZ07d0pTKYiIiIhI54knwc2aNUOqqS7nBN0DIUaOHFnj/srrHD8JSUlJ0rJm1bl/qTEiIiL6G2Al2GB1sk6wKXNycqr1Y5mfhMaNG9d1F4iIiOhJYxJsMI4gEREREdU7rAQTERERmRpWgg3GESQiIiKieoeVYCIiIiJTw0qwwZgEExEREZkaJsEG4wgSERERUb3DSjARERGRqWEl2GAcQSIiIiKqd1gJJiIiIjI1rAQbjEkwERERkalhEmwwjiARERER1TusBJNJcHISE1fx57%2BAhg0BtVpMG1AqxcT19dV/FSE1VVxsAMjIENdGhw5i4paV/fWqUolpw5Q5OIiJa2Wle23QADA3F9MGAJSXGz%2BmTKZ71Wp1X1SV6KqmTPbX38PfBSvBBuMIEhEREVG9w0owERERkalhJdhgTIKJiIiITA2TYINxBImIiIio3mElmIiIiMjUsBJsMI4gEREREdU7rAQTERERmRpWgg3GESQiIiIyNXK58b8MpFKpEB0djaCgIISEhGDt2rU1Hjtx4kT4%2BPjofe3fv1/a/5///AedO3eGUqlEdHQ0SkpKDO7f/VgJJiIiIiKDffTRR/jtt9%2Bwbt06XL58GTNnzoS7uzt69uxZ5djMzEwsWbIEHTt2lLY1bNgQALBr1y4kJCRgyZIlcHZ2RlRUFJYsWYLY2Fij9pdJMBEREZGpecqmQ9y9exdbt27FmjVr4OfnBz8/P5w9exYbN26skgSXlZXh4sWL8Pf3h4uLS5VY69evx%2BjRo9GtWzcAwAcffIBx48Zh%2BvTpsLa2Nlqfn64RJCIiIiKTk5GRAbVaDaVSKW1r164dTp48ifL7HkeelZUFmUyGxo0bV4mj0Wjw66%2B/IigoSNrWtm1b3Lt3DxkZGUbtMyvBRERERKZGQCU4Pz8fBQUFettcXFzg6ur60HMLCgrg6OgICwsLaVujRo2gUqlQVFQEJycnaXtWVhbs7OwwY8YMHD16FM888wwmTZqELl26oLi4GCqVSq9NhUIBBwcHXL161QhX%2BRcmwURERESmRkASvHnzZiQkJOhti4yMxKRJkx56bklJiV4CDED6vqysTG97VlYWSktLERISgvHjx2PPnj2YOHEiNm/ejEaNGumdWznW/XEMxSSYiIiIiDBkyBCEhobqbatuzm51LC0tqySpFd9bWVnpbX/77bcRHh4u3Qjn6%2BuL33//HVu2bMHUqVP1zq0cy5jzgQEmwURERESmR0Al2NXVtVZTH6rj5uaGGzduQK1WQ6HQpZcFBQWwsrKCvb293rFyuVxKgCs0bdoU586dg4ODAywtLXHt2jU0a9YMAKBWq1FUVFTrhLy2eGMcERERERmkZcuWUCgUSEtLk7YdP34c/v7%2BkN%2BXsM%2BaNQtRUVF62zIyMtC0aVPI5XL4%2B/vj%2BPHj0r60tDQoFAr4%2Bvoatc9MgomIiIhMzVP2sAxra2uEhYVh3rx5SE9PR0pKCtauXYtRo0YB0FWFS0tLAQChoaH4v//7P%2BzYsQPnz59HQkICjh8/jpEjRwIAhg8fjn//%2B99ISUlBeno65s2bh8GDB3M6BBEREVG995StEwwAUVFRmDdvHkaPHg07OztMmjQJ3bt3BwCEhIQgPj4eAwYMQPfu3TF37lwkJibi8uXLaN68OT777DN4eHgAAF599VVcunQJsbGxKCsrQ/fu3TF9%2BnSj95dJMBEREREZzNraGosXL8bixYur7Dt9%2BrTe94MGDcKgQYNqjDV%2B/HiMHz/e6H2s7Ol7GyFAWVkZtmzZUuvjfXx8cOTIESF9CQ8Px4oVKwDo5sTMmjULALBixQqEh4cLabOu5Obm4sCBA3XdDSIior%2Bfp2w6hCmqF1e8c%2BdOrFq1qq67UcXs2bMxe/bsuu6GMNHR0UhPT6/rbhARERFVUS%2BmQ2i12rruQrUaNGhQ110gIiIiU1QPK7fG9lSNYExMDN566y29bXFxcZg%2BfTquXr2KyZMno3379ggODsb8%2BfOlhZS3b99eZXHnimkHR44cQVRUFC5dugQfHx9cvHhRb0oCAFy8eFHaV%2BHYsWPo3r07AgICMHnyZNy8eVPad%2BbMGYSHh6NNmzbo0aMHNm7c%2BFjXW3k6RGUqlQrDhg3D2LFjpWv88ssvERoaCqVSifDw8Cpzax7k/PnzGDduHJRKJbp27Yr169dL%2BzIzMzFu3DgEBgaic%2BfOSEhIkJ7xXd0UjdDQUGzfvh2AbowTExMxbtw4aSx%2B%2BOEH6dqOHj2KhISEv900DyIiojrH6RAGe6oqwa%2B%2B%2BirGjx%2BP27dvw87ODuXl5di1a5d0p6Gnpyc2bNiAwsJCzJkzB4AucX4QpVKJ6OhorF27FklJSXrPrn6QjRs3YtGiRXB2dkZ0dDTi4%2BOxaNEilJaW4s0330T//v0RFxeHrKwszJkzB7a2tggLCzN4DMrLy/Hee%2B%2BhvLwcCQkJsLCwwL59%2B5CQkIC4uDh4e3tjx44dGDVqFHbv3l1lsen7qVQqjB07Fn5%2BftiyZQtyc3Mxbdo0NG7cGAEBARg%2BfDhCQ0OxdetWZGdnIyYmBnZ2dhgzZkyt%2Brtq1SrMnTsXc%2BfOxdKlSzFnzhzs27cPs2fPRk5ODpRKJSZMmPBIY1Dds8sVito9u/xRmZnpvwph5HUNJV5e%2Bq%2BmpGJMRI0NADRuLCaum5v%2BqwAKhZj/jMzM5HqvQtz3ZCijsbTUfxVFJhMXU0Ts6toRFVd0/4mesKcqCQ4ODkbDhg2xb98%2B9OvXD7/88gvu3bsHAMjLy8OWLVukpC82NhYTJ06UHq9XEwsLCzRo0ABmZmaP9KSRyMhIdOnSBYAu0Y6IiEBMTAySk5Ph7OyMKVOmAAC8vLxw6dIlrF%2B/3ihJcFxcHM6fP48vvvgCNjY2AIDPPvsMEyZMQLdu3QAAU6ZMwcGDB/G///3voVXWQ4cOobCwEAsXLoSdnR2aN2%2BOmJgYyOVyfPPNN7C2tkZcXBwUCgWaNWuGgoICfPrpp7VOgrt06YIBAwYAACZOnIjXXnsNBQUFcHNzg7m5OWxsbODg4PBIY1Dds8vfeScS77778GeXP677HmZjXJs2CQwOYOFCsfFFEj02Io0dKyy0o7DIOvb2xl1rU4/j8%2BJiA%2BLe3DwJ5uZ13QPDiH4DIpKosf8zR6kT9bBya2xPVRIsl8vRq1cvfPfdd%2BjXrx%2BSk5PxyiuvICsrC15eXnpVz8DAQKjValy4cEFIX/z9/aU/t2rVSmorKysLGRkZUCqV0n6NRgMzI5QSU1NTcezYMQQEBOhda2ZmJpYsWYJPPvlE2qZSqZCTk/PQmNnZ2fD29oadnZ207fXXXwcAzJ07F35%2BftLjDQFd5bygoADFxcW16rNXpSpkRRtqtbpW59akumeXKxQuuHHDoLDVMjPTJcDFxYBGY/z4AOD4znAxgb28dAlwdDRQi5%2BFx5KRISaur68uAR4%2BXFwbD1h6xyBubroEeO1aIC9PSBM33npXSFwzMzns7a1RXFwCjaZcSBuO168IiQtLS10CnJsLqFRi2gCAJk2MH1Mm0yVh9%2B4BIu9RKRfzdwqZTDf%2BKpW4/ov8OK5i7Inu81QlwQDQp08fhIeH4/bt29izZw%2BWLFmCM2fOVDlO82fGotFoIKvmI5pHScQ01WQ/lZPaihvrzM3NoVar0bFjR8TGxtY6fm3Z2toiISEBEyZMQFJSkrR%2BnkajQXR0NDp27Kh3fOXEtiaVE9z7WVbzrr5iPnBtx9W8mnfXht6IWN2zywsKAANz6wfSaATGF5XkVcjJEddGaqqYuBUyMsS10aGDmLgV8vJ0CZkAarWgZOZPGk25uDb%2BfCKUMCqV2DZEJqlarfj4IonuPz0aVoIN9tSNYEBAANzc3LBmzRpotVq0b98e3t7eyMnJQVFRkXRcxXOkmzRpAnNzc9y5c0fap9Vq9W5yuz%2BZs7Cw0Ds%2Bt5r/yCon3unp6TA3N4eHhwe8vb2RnZ0NDw8PeHp6wtPTE2lpadiwYYPB196iRQu88MILmDhxIpYuXSpdr7e3N65evSq15%2BnpiVWrVuk9n7smXl5eOH/%2BPEpKSqRtixcvxvz58%2BHt7Y3ff/9dmnIC6KrRTk5OcHBwqDKud%2B7cQWFhocHXSURERAbijXEGeyqvuHfv3vj888/Rs2dPmJmZoVOnTmjcuDFmzJiB06dP4%2Beff0ZcXBz69OkDe3t7tG7dGkVFRdiwYQNyc3MRHx%2Bvt5qDtbU1bt68iZycHKjVarRu3RrJyclIT09Heno6li9fXqUPy5Ytw%2BHDh5GWlob58%2Bdj6NChsLa2Rr9%2B/VBaWorY2FhkZmbiwIEDWLBgAZydnY12/aNHj0bDhg2l6Q8RERFYt24dduzYgQsXLmDJkiVITk5Gs2bNHhorJCQEjRo1kvq7d%2B9efPnllwgJCUHfvn1RVlYm7UtJScGKFSswbNgwyGQy%2BPv7IyMjA8nJycjOzkZsbCzkj/CPxMbGBjk5Obh%2B/fpjjwURERGRCE9tEqxSqdC7d28AuqkJK1euBAAMHjwY7733Hl566SV8%2BOGHAHTVzpkzZyIxMRFhYWHQarXo0aOHFK9Dhw7w9PRE3759cerUKURERKBVq1YYOXIkpk2bhrfffrtKHyIiIjB79mxERERAqVTi/fffB6CbgrBmzRrk5OQgLCwMMTExGDFixCOvgPAgFhYWiIqKwtatW/Hrr7%2Bid%2B/emDp1KpYvX44%2Bffrg8OHDSExM1JuPWxOFQoGVK1ciPz8f/fv3x4IFCzBjxgx07doVdnZ2%2BOyzz3DhwgWEhYUhLi4Oo0ePRmRkJACgY8eOGDNmDGJjYzF06FA0b94cAQEBtb6OQYMG4YcffsAbb7zxuENBRERE1WEl2GAy7VP4JIkff/wRc%2BbMwd69e6udl0r1z30rphmNQgE4OgI3boibE%2BzSI1BM4Cdxc5mo%2BbpKJXDiBBAYKK6NiRPFxG3cGIiKAuLjhc0JLvhgsZC4CoUcjo62uHHjjrA5wS5554XEhZUV8PzzwLlzYucEN29u/JgyGWBhAZSVme6NcVZWunEX1f8H3L9iMNE3xtXVqh%2BJicaPKer35lPqqboxLj8/H8ePH8fq1asxcOBAJsBEREREJMRTlQTfunUL0dHRaNu2LSIiIuq6O49swYIFSEpKqnH/hAkTqjwRz1DBwcHSU%2BWqs3PnTri7uxu1TSIiIqpj9XD6grE9VUlws2bNkCp6SSaBJk6ciJEjR9a4/2FPd3scSUlJ0rJm1RHxlDUiIiIiU/dUJcGmzsnJqdaPZTaWxqb89CQiIiJ6PKwEG4xJMBEREZGpYRJsMI4gEREREdU7rAQTERERmRpWgg3GESQiIiKieoeVYCIiIiJTw0qwwZgEExEREZkaJsEG4wgSERERUb3DSjARERGRqWEl2GAcQSIiIiKqd1gJJiIiIjI1rAQbjEkwERERkalhEmwwJsFkEhSCflLNzPRfhejSRUxcDw/da7t2gJubmDY6dBATt3Fj3eugQeLaSEwUE1epBKKigK1bgdRUMW18sFhM3CfB0lJMXAuLv161WjFtAOJ%2B2QCCf9EAUKvFxJXJdK9yudixJ3rCmAQTERERmRpWgg3GESQiIiKieoeVYCIiIiJTw0qwwZgEExEREZkaJsEG4wgSERERUb3DSjARERGRqWEl2GAcQSIiIiKqd1gJJiIiIjI1rAQbjEkwERERkalhEmwwjiARERER1TusBBMRERGZGlaCDcYRJCIiIqJ6h5VgIiIiIlPDSrDBOIJEREREpkYuN/6XgVQqFaKjoxEUFISQkBCsXbu2xmO///57vPbaa1Aqlejbty/27t2rtz8oKAg%2BPj56X3fu3DG4j5WxEkxEREREBvvoo4/w22%2B/Yd26dbh8%2BTJmzpwJd3d39OzZU%2B%2B4jIwMREZGYsaMGejSpQsOHTqEyZMnIykpCb6%2BvsjLy8OtW7eQkpICKysr6TwbGxuj9pdJMBEREZGpecqmQ9y9exdbt27FmjVr4OfnBz8/P5w9exYbN26skgR/88036NChA0aNGgUA8PT0xL59%2B5CcnAxfX19kZmbCxcUFjRs3FtpnJsFEREREZJCMjAyo1WoolUppW7t27bBq1SqUl5dDXilp79%2B/P%2B7du1clxq1btwAA586dg7e3t/A%2BMwkmIiIiMjUCKsH5%2BfkoKCjQ2%2Bbi4gJXV9eHnltQUABHR0dYWFhI2xo1agSVSoWioiI4OTlJ25s1a6Z37tmzZ3H48GEMHToUAJCZmYmSkhKEh4cjOzsbLVu2RHR0tNETYybBRERERKZGQBK8efNmJCQk6G2LjIzEpEmTHnpuSUmJXgIMQPq%2BrKysxvMKCwsxadIkBAYG4qWXXgIAZGVl4ebNm3jvvfdgZ2eHNWvWYMyYMdi5cyfs7Owe9bJq9HRNKLlPWVkZtmzZUuvjfXx8cOTIEYE9%2BsvFixfh4%2BODixcvPlLblY8LDQ3F9u3bAQDh4eFYsWKFuA7XgcOHDyMzM7Ouu0FERES1MGTIEGzfvl3va8iQIbU619LSskqyW/F95ZvbKrt27RpGjx4NrVaL5cuXS1Mm/v3vf2PHjh148cUX0aZNG3z88cdQqVTYv3%2B/AVdX1VNdCd65cydWrVqFwYMH13VXHurQoUNo2LDhI52TlJRk9DsdnyZjxozB%2BvXrq3zsQURERAYSUAl2dXWt1dSH6ri5ueHGjRtQq9VQKHTpZUFBAaysrGBvb1/l%2BLy8POnGuPXr1%2BtNl7CwsNCrKltaWsLDwwN5eXmP1beaPNWVYK1WW9ddqDUXF5cqHwM8jJOTU43vjoiIiIhMRcuWLaFQKJCWliZtO378OPz9/fVuigN0K0m88cYbkMvl%2BOKLL%2BDm5ibt02q1ePnll6VPyiuOP3/%2BPJo2bWrUPhslCY6JicFbb72lty0uLg7Tp0/H1atXMXnyZLRv3x7BwcGYP3%2B%2BVB7fvn07QkND9c6rmBZw5MgRREVF4dKlS9K0g/unDNw/JQEAjh07hu7duyMgIACTJ0/GzZs3pX1nzpxBeHg42rRpgx49emDjxo21vsZ79%2B4hLi4OQUFB%2BMc//oEDBw7o7a88zeHw4cN47bXX4O/vj5deeglffvlltTErT4eo7MKFC3jxxRexfPlyALqPE%2BbPn4/g4GAEBwfj/fffR1FRUa37fvDgQfTv3x8BAQHo168fDh8%2BLO3bv38/%2BvfvjzZt2qB3797YvXu3tO9h4%2B3j44Ovv/4affr0QevWrTF8%2BHDk5uZK1wYAo0aN%2BttN8yAiIqpzT9nDMqytrREWFoZ58%2BYhPT0dKSkpWLt2rVTtLSgoQGlpKQBg9erVuHDhAhYvXiztKygowK1btyCTydC1a1cpFzx79ixmzJiBZ555Bl26dDFszO5jlOkQr776KsaPH4/bt2/Dzs4O5eXl2LVrF%2BbNm4fRo0fD09MTGzZsQGFhIebMmQNAlzg/iFKpRHR0NNauXYukpCS9MvmDbNy4EYsWLYKzszOio6MRHx%2BPRYsWobS0FG%2B%2B%2BSb69%2B%2BPuLg4ZGVlYc6cObC1tUVYWNhD465YsQL79%2B9HYmIiFAoFZs2aVe1xGo0GU6ZMwZgxY9C3b1%2BcOHECM2fORFBQEJ5//vmHtlNYWIhx48ahV69eePfddwEAn3zyCX777TesWbMGlpaWWLZsGSZPnox169Y9NN7Zs2cxceJEvPPOO1KS%2B/bbb2P37t04d%2B4cJk2ahPfffx9dunTB999/j6lTp2Lz5s1o3br1Q2NXjEtcXBycnZ0xefJk/POf/8TSpUuRlJSEjh07YsWKFejUqVOtYlWo7u5UKysXuLg83kc0D1Lxb17ocoseHmLiVnxk9ZgfXdXKA25mMEjFu/5K7/6NrtIyPUbl66v/KoBCIeYH0sxMrvcqhKWlmLjm5vqvVJVMJjauqPj0eJ6ydYIBICoqSsr97OzsMGnSJHTv3h0AEBISgvj4eAwYMAC7du1CaWkpBg0apHd%2B//79sWjRIkyfPh0KhQLTpk3D7du30aFDB/zrX/%2BCmZmZUftrlCQ4ODgYDRs2xL59%2B9CvXz/88ssv0vpveXl52LJlizRfNjY2FhMnTsTUqVMfGNPCwgINGjSAmZkZXFxcat2XyMhI6Z1CTEwMIiIiEBMTg%2BTkZDg7O2PKlCkAAC8vL1y6dAnr169/aBKs1WqxdetWzJw5Ey%2B88AIAIDo6GuPHj69y7K1bt1BUVIRGjRrBw8MDHh4ecHV1rdU13L17F%2BPHj0ebNm2kNwklJSX44osvsG3bNvj4%2BADQPZElODgYp0%2BflrbVJCkpCYGBgXj77bcBAOPHj8fdu3dRXFyMjRs3okePHhgzZgwAwNvbG%2Bnp6Vi7di0%2B%2BeSTh/YXACIiItCxY0cAwLBhw6TqesWbloYNG8LW1rZWsSpUd3fqO%2B9E4t13H3536uMy4s2mVU2bJjA4gPBwsfFFGjtWXOyoKHGxAWDTJmGhHYVF1rG3txYX3PHR/r0/smefFRtfJCP/B/7E45vyGxBRfa9mrdv6zNraGosXL5YqvJWdPn1a%2BvN33333wDiWlpaYNWtWjQVHYzFKEiyXy9GrVy9899136NevH5KTk/HKK68gKysLXl5eejeMBQYGQq1W48KFC8Zougp/f3/pz61atZLaysrKQkZGht4izhqNplbvKm7cuIHCwkK0bNmy2nYqc3BwwLBhwxATE4OVK1eiW7dueP3112t109yGDRugVqsRHBwM2Z/vuHNzc3Hv3j1p7bwK5eXlyMnJeWgSnJ2dDT8/P71tFW8EMjMzq8RVKpXYtm3bQ/tawdPTU/qznZ1dtYtfP6ohQ4ZUmSZjZeWC4mKDQ1chl%2BsS4Nu3gfJy48cHAPs1S8UEdnXVJcAbNgD5%2BWLaEFkJHjsWWLsWMPKNDpKtW8XE9fXVJcDDhwMZGUKauLH3ByFxzczksLe3RnFxCTQaMT/wjreuC4kLc3NdAnzlitjE47nnxMQ1MwM0GjGxK4iKL5Ppxv/ePUDUvToiq8wVff%2B7eQorwabGaKtD9OnTB%2BHh4bh9%2Bzb27NmDJUuW4MyZM1WO0/z5j1Sj0UiJXmVqtbrWbWqq%2BQdfOamtuLHO3NwcarUaHTt2RGxsbK3j36/yjXrmD3hXOW/ePIwYMQIpKSlISUnB5s2bsXLlyofOZfHz88OYMWMwffp0DBgwAM2aNZOucdOmTVVWknB2dn5onyvu0KyOZTUfW5aXl6O8hmywuvF%2B0Dg8ruruTr1xQ%2Bz/H%2BXlAuNXmrMuRH6%2BuDZUKjFxK%2BTlAX/OIze61FQxcStkZAhrQ60W9I7sTxpNubg2RP/M3Lsnvg1TJfpmcq3WNJPgvysmwQYz2ggGBATAzc0Na9asgVarRfv27eHt7Y2cnBy9m7jS0tKgUCjQpEkTmJub486dO9I%2BrVard5Pb/UmyhYWF3vG51fznWTnxTk9Ph7m5OTw8PODt7Y3s7Gx4eHjA09MTnp6eSEtLw4YNGx56bY6OjmjUqBF%2B/fVXadsff/xR7bEFBQX44IMP4OnpiYkTJ2Lbtm3o0KED9u3b99B2QkJC0KtXL3Ts2BEffvghAKBx48YwMzNDUVGR1G87OzvEx8fj%2BvWHV1w8PT2RcV%2B1aujQodi5cye8vb1x8uRJvX2pqanSE1lqM95EREREpsiobyN69%2B6Nzz//HD179oSZmRk6deqExo0bY8aMGVNT/GwAACAASURBVDh9%2BjR%2B/vlnxMXFoU%2BfPrC3t0fr1q1RVFSEDRs2IDc3F/Hx8XqrOVhbW%2BPmzZvIycmBWq1G69atkZycjPT0dKSnp0urJ1S2bNkyHD58GGlpaZg/fz6GDh0Ka2tr9OvXD6WlpYiNjUVmZiYOHDiABQsW1KqaKpPJMGLECCxfvhw//fQTfv31V8THx1d7bMOGDbFnzx4sXLgQFy5cwLFjx5CRkYFWrVrVehyjo6Nx/Phx6ckogwYNwrx583DkyBGcO3cOM2bMwPnz5%2BFRixuuhg0bhl9%2B%2BQWff/45zp8/j9WrV%2BPs2bMICgrCmDFjsGvXLqxbtw45OTn4z3/%2Bgz179mDYsGEAUKvxfhAbGxucPXtWehY4ERERGclTtjqEKTJ6EqxSqdC7d28AuqkJK1euBAAMHjwY7733Hl566SWpyunl5YWZM2ciMTERYWFh0Gq16NGjhxSvQ4cO8PT0RN%2B%2BfXHq1ClERESgVatWGDlyJKZNmybd7FVZREQEZs%2BejYiICCiVSrz//vsAID12LycnB2FhYYiJicGIESMwYcKEWl3bW2%2B9hbCwMEydOhUTJkyockdjBQsLC6xcuRIZGRno168fpkyZgoEDB9Z4fHW8vb0RHh6ORYsW4fbt25g1axY6duyId999F4MHD4ZCoaj1XZJNmjTBihUrsG3bNvTp0we7du3CqlWr4ObmhoCAAHz00Uf473//iz59%2BmDbtm345z//Kd3oVpvxfpDw8HB89NFHXCKNiIiInjoyrRGfSPHjjz9izpw52Lt3b7XzfYke140bYuKamQH29kBxsbg5wY4fPngllMfm4aFbeWLpUtObE9y4sW71hvh4cXOCExPFxFUqgRMngMBAYXOCC/IF3AUK3dJrjo62uHHjjrA5wS5FV4XEhaUl0KQJcOGC2DnBRl6MX/Ikbox7hHtqHolMBlhY6G6UFTUnWGQVUvSNcXW1aoaI3z%2BilpZ8Shnlxrj8/HwcP34cq1evxsCBA5kAExEREYlUD6cvGJtRkuBbt24hOjoabdu2RUREhDFCPlELFixAUlJSjfsnTJhQ5Yl4T4P09HSMHj26xv3u7u7YuXPnE%2BwRERERkWkwShLcrFkzpIpejkigiRMnYuTIkTXur80av3XB19cXO3bsqHH/g5ZHIyIiIhPGSrDBmCVB93Sz2j6W%2BWliYWGh97AKIiIiIqodJsFEREREpoaVYIMxCSYiIiIyNUyCDcYRJCIiIqJ6h5VgIiIiIlPDSrDBOIJEREREVO%2BwEkxERERkalgJNhiTYCIiIiJTwyTYYBxBIiIiIqp3WAkmIiIiMjWsBBuMI0hERP/P3r3H51j4fxx/7bzZjGFbRDOSJGaO0ZKWynGN9EW1GorKiBAzhzLS16kv5ptUFDqIhlIOqb596TAapmRijFna5nzawbb798d%2Bu7/uNtzr3mXu9n4%2BHnvcdl3X/bk%2B92Xcn/uzz3VdIiKVjjrBIiIiIvZGnWCbOZhMJlNFJyFyLVlZ5wyJ6%2BzsiI%2BPJ6dOXSA/v9CQfRhFuVcM5V4xlHvFsef8jc7d17dquce0Wlpa%2BcesV6/8Y97A9DFCRERERCodjUOIiIiI2BuNQ9hMR1BEREREKh11gkVERETsjTrBNlMRLCIiImJvVATbTEdQRERERCoddYJFRERE7I06wTbTERQRERGRSkedYBERERF7o06wzVQEi4iIiNgbFcE20xEUERERkUpHnWARERERe6NOsM10BEVERESk0lEnWERERMTeqBNsMxXBIiIiIvZGRbDNdARFREREpNJRJ1hERETE3qgTbDMdQRERERGpdNQJFhEREbE36gTbrMKPYF5eHh9//LHV2zdu3JiEhARDcjl58iRPPPEEzZo1Y%2BzYsYbs40rGjRvHuHHjADCZTLz//vvXdf9GSEtL49tvv63oNERERP5%2BHB3L/8tGubm5jB8/ntatWxMSEsLixYuvuO2vv/7Ko48%2BSlBQEI888gi//PKLxfp169bRuXNngoKCGDp0KCdPnrQ5vz%2Br8CL4888/Z%2BHChRWdBgCffvopqamprFmz5roXwTExMcTExACwfft2pkyZcl33b4Tx48eze/fuik5DREREroMZM2bwyy%2B/8N577zF58mTi4uLYsGFDie0uXrzI4MGDad26NfHx8QQHBzNkyBAuXrwIwO7du4mJiSEqKooVK1Zw9uxZoqOjyz3fCh%2BHMJlMFZ2C2fnz56lfvz4NGza87vuuWrWq%2Bc830jERERGRG9ANNg5x8eJFVq5cyVtvvUXTpk1p2rQp%2B/fv5/3336dLly4W237xxRe4ubnx0ksv4eDgQExMDP/973/ZsGEDvXv3Zvny5XTt2pXw8HCgqLi%2B7777SEtLo169euWWs9VHcMKECTz77LMWy2JjYxkzZgx//PEHL7zwAm3btqVdu3ZMnTqVvLw8AOLj4wkNDbV4XkREBPPnzychIYHo6GjS09Np3LgxR48eNa8rdvToUfO6Ytu3b%2BfBBx8kKCiIF154gTNnzpjX/fbbb0RERNC8eXMeeughq8cK5s%2Bfz/z589m%2Bfbt55OL8%2BfNER0fTvn177rzzTrp06cLmzZsBmDVrFk888YRFjDlz5hAZGQnAmTNnmDhxIh06dKBVq1aMGTPGnGdCQgKhoaFMnjyZVq1asWjRIvM4xNGjR3nyyScBy9GPjz76iNDQUIKDg4mIiGDfvn1WvS6Aw4cPM2jQIIKDg%2BnUqRNLly41r0tJSWHQoEG0bNmSe%2B65h7i4OAoLC83HJCIiwiJWaGgo8fHxQNHf4xtvvMGgQYPMx3vLli1A0XjHtm3biIuLKxFDRERE/l6Sk5PJz88nODjYvKxVq1YkJSWZ64piSUlJtGrVCgcHBwAcHBxo2bIlu3btMq9v3bq1efvatWtTp04dkpKSyjVnqzvB3bt3Z/DgwZw/fx4vLy8KCwvZuHEjL7/8Mk899RQBAQEsW7aMkydPMnHiRKCocL6a4OBgxo8fz%2BLFi1m1ahU1atSwKpf333%2Bf1157jZo1azJ%2B/HimT5/Oa6%2B9Rk5ODs888wy9evUiNjaWgwcPMnHiRDw9Pc2fJq5k4MCBXLx4kZ07dzJ//nyqVavG5MmTOXToEIsXL8bDw4O3336bmJgYOnbsSPfu3XnnnXc4ceIENWvWBGDjxo08/fTTAERFRZGdnW0e9Xj55ZcZN24cb7zxBgDp6enk5eURHx%2BPi4sL8%2BbNA4r%2BoufPn8%2BwYcPYunUr1apV4%2BuvvyYuLo7Y2FgCAwNZs2YNTz75JJs2baJatWpXfV25ubkMHDiQpk2b8vHHH5OWlsaoUaOoV68eQUFBPPbYY4SGhrJy5UoOHTrEhAkT8PLyMhfz17Jw4UImT57M5MmTmT17NhMnTuTrr78mJiaG1NRU8684yiIzM5OsrCyLZc7OVfDz8ytTHGs4OTlaPNoT5V4xlHvFUO4Vx57zt%2Bfcr8mATnBp77%2B%2Bvr5Wvf9mZWXh4%2BODq6ureVmtWrXIzc3l9OnTFjVeVlYWt956q8Xza9asyf79%2B815/HmfNWvW5I8//ijza7oaq4vgdu3amQuysLAwfvrpJy5dugRARkYGH3/8sbkgmzRpEs899xwjR468akxXV1eqVq2Kk5MTvr6%2BVicdFRXFvffeCxQV2gMGDGDChAmsX7%2BemjVrMmLECADq169Peno6S5cuvWYR7OnpSZUqVXBxcTHn0qZNGwYMGMBtt90GFBXKK1eu5MSJEzRp0oT69euzefNm%2Bvbty759%2B0hPT%2BeBBx4gOTmZbdu2sWHDBgIDAwGYOXMm3bp14%2BDBg%2BZ9Pv300wQEBFjk4eTkZD6OxXm8/fbbDBkyhPvuuw%2BAESNG8N///pdPP/30ml3WrVu3cvLkSV599VW8vLxo1KgREyZMwNHRkXXr1uHh4UFsbCzOzs40bNiQrKwsFixYYHURfO%2B999K7d28AnnvuOR5%2B%2BGGysrLw9/fHxcWFKlWqUL16datiFVuxYgVxcXEWy4YOHcrw4cPLFKcsvL09DIttNOVeMZR7xVDuFcee87fn3K/EhEO5xyzt/TcqKophw4Zd87nZ2dkWBTBg/r54OuBa2xZvl5OTc9X15cXqItjR0ZGuXbuyYcMGwsLCWL9%2BPQ888AAHDx6kfv36Fh3Jli1bkp%2Bfz5EjR8o12WLNmjUz//mOO%2B4w7%2BvgwYMkJydbtOILCgpwcnL6S/sJDw9n8%2BbNfPzxxxw8eJA9e/aYYwJ069aNTZs20bdvXzZt2kSHDh2oXr0633//Pd7e3uYCGKBhw4ZUq1aNgwcPmud/69ata1UeKSkpzJw5kzlz5piX5ebmkpqaes3nHjp0iMDAQLy8vMzLHnnkEQAmT55M06ZNcXb%2B349BcHAwWVlZnD171qrc6tevb/5z8T7y8/Oteu6V9O3bt8QIjbNzFU6dumBT3NI4OTni7e3B2bPZFBQUXvsJNxDlXjGUe8VQ7hXHnvM3OncfH89yj1mRSnv/tbZJ6ebmVqJILf7e3d3dqm2Lt7vSeg%2BP8v0wU6YT43r06EFERATnz5/nyy%2B/ZObMmfz2228ltisuEgsKCszzHpcrS5FUHOtylxe1xSeRubi4kJ%2BfT/v27Zk0aZLV8a/mpZdeYufOnTz88MP0798fX19f%2Bvbta17frVs33nzzTc6ePcumTZsYNGgQQIlPL5e/lstfj5ubm1V5FBQUMH78eNq3b2%2Bx/PLC9kouL3D/rLT9F8/tWPt35%2BLiUmIbW0/s8/PzK/FrkKysc%2BTnG/efb0FBoaHxjaTcK4ZyrxjKveLYc/72nPuVFBrwckp7/7WWv78/p06dIj8/31x7ZGVl4e7ujre3d4ltjx8/brHs%2BPHj5n1faX1ZpgasUaaBkqCgIPz9/XnrrbcwmUy0bduWwMBAUlNTOX36tHm7Xbt24ezszC233IKLiwsXLvyvg2cymSxOcvtzoeXq6mqxfVpaWok8Li%2B8d%2B/ejYuLC3Xr1iUwMJBDhw5Rt25dAgICCAgIYNeuXSxbtqwsLxMoulLEunXreP311xk%2BfDgPPPCA%2BcS24iKvYcOGNGzYkI8%2B%2BojU1FQ6d%2B4MQGBgIGfPnrUYfThw4ADnz5%2B36A5fyZ%2BPSWBgIH/88Yf5NQUEBLBw4ULzAPnV1K9fn8OHD5OdnW1e9s9//pOpU6cSGBjInj17zGMtADt37qRGjRpUr169xN/dhQsXDLlOn4iIiJRNYWH5f9miSZMmODs7W9QmiYmJNGvWDMc/zS8HBQWxc%2BdOcz1lMpnYsWMHQUFB5vWJiYnm7Y8dO8axY8fM68tLmaequ3XrxpIlS%2BjSpQtOTk7cfffd1KtXj5deeol9%2B/bx448/EhsbS48ePfD29ubOO%2B/k9OnTLFu2jLS0NKZPn25xNQcPDw/OnDlDamoq%2Bfn53Hnnnaxfv57du3eze/du8wljl3v99df54Ycf2LVrF1OnTqVfv354eHgQFhZGTk4OkyZNIiUlhW%2B//ZZp06aZT1wrC1dXVzw8PNi0aRNHjx5ly5Yt5mv3Xt6i7969O2%2B88QYdO3Y0d2YbNmxIx44dGTt2rPl1jB07ljZt2pjni6%2BmuN3/yy%2B/kJuby4ABA3jvvfdYs2YNR44cYebMmaxfv96qS7mFhIRQq1Yt8zH56quv%2BOijjwgJCaFnz57k5eWZ123evJn58%2BfTv39/HBwcaNasGcnJyaxfv55Dhw4xadKkEj/IV1OlShVSU1M5ceKE1c8RERER%2B%2BPh4UF4eDgvv/wyu3fvZvPmzSxevNh8xausrCxycnIA6NKlC2fPnmXatGkcOHCAadOmkZ2dTdeuXQHo378/a9euZeXKlSQnJ/PSSy/RqVOncr08GvzFIjg3N5du3boBRaMJ//73vwH4xz/%2BwYsvvsj9999vLhjr16/P2LFjeeONNwgPD8dkMvHQQw%2BZ4911110EBATQs2dP9u7dy4ABA7jjjjt44oknGDVqFM8//3yJHAYMGEBMTAwDBgwgODiY0aNHA0XjAW%2B99RapqamEh4czYcIEHn/88TJfnQCKiuCZM2eyceNGunfvzmuvvcZzzz2Hr68ve/futTgeFy9epHv37hbP/%2Bc//0m9evWIjIxk0KBBNGrUiAULFli178aNG3P33XfTr18/vv32W7p168bIkSOZN28ePXr04IcffuCNN96wmMe9EmdnZ/7973%2BTmZlJr169mDZtmvmHycvLi7fffpsjR44QHh5ObGwsTz31FFFRUQC0b9%2BeyMhIJk2aRL9%2B/WjUqFGZPoU9%2BuijbNmyxXzFDBERESkfN1onGCA6OpqmTZvy1FNP8corrzBs2DAefPBBoKgp98UXXwBF9dqbb75JYmIivXv3JikpiUWLFlGlShWg6PykKVOmsGDBAvr370%2B1atWYPn267Qn%2BiYOpjAOc3333HRMnTuSrr74qdWZUxAhZWecMievs7IiPjyenTl2wu3kx5V4xlHvFUO4Vx57zNzp3X9%2Bq197IILm55R/TylOV/jasPjEuMzOTxMRE3nzzTfr06aMCWERERKSCGHFiXGVjdRF87tw5xo8fT4sWLRgwYICRORli2rRprFq16orrhwwZUuKOePagXbt2V71u3ueff06dOnWuY0YiIiJiNBXBtrO6CG7YsCE7d%2B40MhdDPffccyVuc3y5a9157Ua1atWqErcjvJwRd1kTERERsXdluk6wPatRo4bVt2W2J%2BV9pqSIiIjc%2BNQJtt3f8GbaIiIiIiJXV2k6wSIiIiJ/F%2BoE205FsIiIiIidURFsO41DiIiIiEilo06wiIiIiJ1RJ9h26gSLiIiISKWjTrCIiIiInVEn2HYqgkVERETsjIpg22kcQkREREQqHXWCRUREROyMOsG2UydYRERERCoddYLFLgwZUtWQuA0awKxZMG2aJwcPGrIL4l/cakxgT0/wCcYn9Te4cMGYfVSvbkxcd3fwuRWfE8cgJ8eYfbi5GRfXxxOfcycgN9eQXWRVv8mQuNeDr5%2B3MYGDg2HHDnzuvwd27jRmHwBnz5Z/zP9vN/m4Yuy7rrNBwR0cAPDxcAKTQb0zo9qajkX5%2Brg7ghG7uHCh6P/iCqBOsO1UBIuIiIjYGRXBttM4hIiIiIhUOuoEi4iIiNgZdYJtp06wiIiIiFQ66gSLiIiI2Bl1gm2nIlhERETEzqgItp3GIURERESk0lEnWERERMTOqBNsO3WCRURERKTSUSdYRERExM6oE2w7FcEiIiIidkZFsO00DiEiIiIilY46wSIiIiJ2Rp1g26kTLCIiIiKVjjrBIiIiInZGnWDbqQgWERERsTMqgm2ncQgRERERqXTUCRYRERGxM%2BoE206dYBERERGpdFQEX2dHjx6lcePGHD16tKJTuS7Wr1/PiRMnKjoNERGRv5XCwvL/qmxUBF9ntWvXZuvWrdSuXbuiUzFceno6I0aMIDs7u6JTERER%2BVtREWw7zQRfZ05OTvj6%2BlZ0GteFyWSq6BRERERESqVOsEFGjhzJ2LFjLZaNGjWKyMhIi3GIs2fPMmbMGFq2bElISAixsbHk5ORQWFhI27Zt%2Beabb8zPf/DBBy1izpkzh9GjR1uVz9q1a%2BnSpQtBQUH069ePX3/91bwuPj6erl270rx5c3r37s327dvN60JDQ4mPjzd/n5CQQOPGjYH/jXZs2rSJzp0706xZM4YMGcLp06cBuP/%2B%2B82Pl8cQERER26gTbDt1gg3SvXt3xo8fz6VLl3BxcSEvL49vvvmG6OhofvjhB/N2MTExXLp0iQ8//JDc3FymTp3KlClTePXVV2nfvj3btm3jvvvuIyMjgyNHjlh0V7/77juefPLJa%2BayZcsWYmJiiImJoUOHDixbtowhQ4bw1VdfsW7dOmJjY5k8eTLNmzcnPj6ewYMHs2HDBvz9/a16rQsXLmTOnDmYTCaee%2B45lixZwsiRI1m5ciWPPvooK1eu5LbbbrP62GVmZpKVlWWxrHp1X2rU8LM6hrVuvtny0RCensbE9fCwfDSCu7sxcd3cLB%2BN4OpqTFwXF8tHAzg7G9OfcHJytHg0RHCwMXFvv93y0SiOBhyb4phGxL6cg4OxcY2KD8Ydm8tzN2IfFVg5VsaitbypCDZIx44dKSwsJCEhgZCQELZu3Yq7uzvt2rUzb3PkyBE2b97Mtm3bqFq1KgCxsbGEh4cTHR1NSEgIH374IQA//fQTd999Nz/%2B%2BCPHjx/HxcWF5ORk7rnnnmvmsmLFCnr06EH//v0BeOmll3BxceHMmTMsW7aMiIgIwsPDARg9ejTbt29n%2BfLljBo1yqrXOnz4cJo3bw5Az549%2BfnnnwGoUaOG%2BdG9DMXUihUriIuLs1j2/PNRvPDCMKtjlNXIkYaFBgwqCooZXRQYqV69is7grzNwrt/HsMhFvL0N/OC0Y4dxsQE%2B%2BMDY%2BEYy8gPr9WDkh1ajGfWB/sIFY%2BLKdaEi2CCurq507tyZTZs2ERISwqZNm3jooYdwvOyTaEpKCoWFhXTs2NHiuYWFhRw%2BfJiQkBAmT57MuXPn2L59O3fffTenTp0iMTERgMaNG5sLzas5dOgQ/fr1s8iteKwiJSWFoUOHWmzfokULUlJSrH6tAQEB5j97eXlx6dIlq59bmr59%2BxIaGmqxbPZsX6yc/CiTm28uKoBffx3S08s/PsCsx3caE9jDo6gATk4Go04%2B/P8PZ%2BXOza2oAE5Lg9xcY/ZhZCe4dm04dgxs/Fm/klNVaxoS18nJEW9vD86ezaagwJg2ks/91/5g/pfcfntRAfzYY0U/80bZsqX8Yzo6Fv17zc42tn3n5GRMXAeHon%2Bzublg1LkeRsV1cCgqgHNyjNtHBbHHTrDJZGL27NmsWrWKwsJC%2BvTpw%2BjRoy1qo8vt2rWL1157jX379uHn58fTTz/No48%2Bal4fFhbGvn37LJ7z2WefWf3bZxXBBurWrRvR0dFMmDCBr7/%2BmgULFlisLygooGrVqnzyySclnuvv74%2B7uzsBAQH89NNP/PTTT/Tq1Yvff/%2BdHTt2kJuba1UXGMDZ%2Bcp/zW6lfLIvKCig8Ar/ugoKCkoscynnXwv7%2Bfnh52c5%2BnD6dNGXUdLT4eBBg4Ib3SnIzjZuHwb%2Byh8oelPNyTEmttFveJcuGVbA53sY%2B%2B5WUFBIfr5B%2B9hp0Ie%2BYsnJxu7DyMrC6MFLo8ctTCbj/l0ZdVyKj4nJZJ9V49/MkiVLWLduHXFxceTn5zNmzBhq1qzJoEGDSmyblZXFM888Q//%2B/XnttdfYs2cP0dHR%2BPr60qlTJwoKCkhNTWX58uXUr1/f/DwfH%2Bt/l6YT4wzUoUMHCgoKWLJkCe7u7rRu3dpifWBgIOfOncPBwYGAgAACAgLIyclhxowZ5OXlARASEsLmzZtJT0/njjvuoHXr1iQmJrJ161ari%2BCAgACSL%2BucFBQUEBoaSmJiIoGBgSQlJVlsn5SURGBgIFBU4F64rMBKS0uz%2BvU7GDk/JiIiUonZ44lxS5cuZfjw4bRu3Zq77rqL0aNH8/7775e67ebNm6lVqxYvvvgi9evXp3v37oSHh/PZZ58BRSfnX7p0iebNm%2BPr62v%2Bulrj789UBBvI2dmZBx98kIULF9KlS5cSRWHDhg255557GD16NLt37zZ/yrl48SLe3t5AURG8du1amjVrhouLC61bt2bPnj2cPn2aFi1aWJVHREQEn376KatXr%2Bbw4cNMnz4dk8lE06ZNiYyMZPny5axZs4ZDhw4xa9YskpOT6dOnDwDNmjVj1apV/PbbbyQkJLB48WKrX7/H/8%2B/JScnWxTSIiIiYht7K4IzMjI4duwYbdq0MS9r1aoV6enpZGZmltj%2BnnvuYfr06SWWnz9/HoADBw5Qu3btUn%2BjbS0VwQbr3r07Fy9epHv37qWunzFjBnXr1iUyMpIBAwYQGBjInDlzzOvbtm2Lg4MDrVq1AqBWrVrccsstdOjQwepPO23atGHy5MksWLCAsLAw9u7dy8KFC3F3d6dbt26MHDmSefPmERYWxrZt21i8eDENGzYEYMSIEXh7e9O7d2%2BmTZvGCy%2B8YPVrr1GjBmFhYYwYMYKVK1da/TwRERG5/jIzM9mzZ4/FV2kF6l9RfNWny8cda9WqBcAff/xRYvu6detaNPtOnDjB559/Tvv27YGic5pcXFwYMmQId999N0888QS7d%2B8uU04OJt3RQOxA797GxG3QAGbNgtGjjZsJjn9xqzGBPT2LLke1c6dxM8HVqxsT190dbr0VDhwwbibYqDPZ3dzgllvgyBHDZoKzqt9kSFxnZ0d8fDw5deqCYTPBvn7ehsQlOLjoyhMtWxo7E3z2bPnHdHQs%2Bvd64YKx7bYy/Bq4TK7HyWVGzgQbfVKiUZfBvIbLbiNQbn75ZX6JqzNFRUUxbJh1V2fKyckhIyOj1HUZGRlERESQnJxs/s14YWEhTZo04f333y8xMvrnuAMHDuTEiROsWbMGDw8PoqOj%2Beabb5g6dSq1a9fm448/5tNPP%2BWLL76w%2Bq68OjFOREREREq9OlNZ7nKblJR0xfsXjBkzBoC8vDzzCEPx%2BU8eV7l84IULF3j%2B%2BedJTU3lgw8%2BMG9bfHMxLy8vAF5%2B%2BWV27NjB2rVrefbZZ63KV0Wwndu4cSPjxo274vpWrVrx9ttvX8eMRERExGhGNLZLuzpTWbRr167EJcuKZWRkMHPmTLKysqhbty7wvxGJKxXa58%2Bf5%2Bmnn%2BbIkSO89957FleBcHZ2NhfAUHQyfoMGDa7YiS6NimA7FxISwpo1a664viw3qRARERH7YG9XfPP396dOnTokJiaai%2BDExETq1KlTauFdWFhIVFQUR48eZdmyZeZzlYpFRETQrl07oqKizNvv27ePxx9/3OqcVATbOU9PTzwraB5JRERExFr9%2B/dn1qxZ3HRT0XkPs2fPZuDAgeb1J0%2BexM3NDU9PT1atWkVCQgJvvPEG3t7e5q6xi4sL1atXJzQ0lAULFtCkSRMCenspfwAAIABJREFUAwNZunQp586do1evXlbnoyJYRERExM7YWycYYNCgQZw4cYKoqCicnJzo06cPkZGR5vV9%2BvShV69eDBs2jI0bN1JYWMiQIUMsYrRt25Zly5YRGRlJbm4uU6dO5fjx4wQFBbFkyRKLEYlrUREsIiIiIoZzcnIiOjqa6OjoUtd//fXX5j%2B/8847V43l4ODAs88%2Ba/VJcKVRESwiIiJiZ%2ByxE3yjUREsIiIiYmdUBNtOd4wTERERkUpHnWARERERO6NOsO3UCRYRERGRSkedYBERERE7o06w7VQEi4iIiNgZFcG20ziEiIiIiFQ66gSLiIiI2Bl1gm2nTrCIiIiIVDoOJpPJVNFJiFxLbq4xcR0cwNUV8vLAqH8Jbo6XjAkM4OIClwyMb1Sr4XoceGcDf9Hl5AQFBYaFzzp50ZC4zs6O%2BPh4curUBfLzjfm79XU3JCw4OoKnJ1y4YGwLzNu7/GMGB8OOHdCyJezcWf7xi736qjFx/f1h4EBYvBgyMozZx6ZNxsRt1AgWLYLBg2H/fmP28c03xsS9hpUryz/mo4%2BWf8wbmcYhREREROyMxiFsp3EIEREREal01AkWERERsTPqBNtOnWARERERqXTUCRYRERGxM%2BoE205FsIiIiIidURFsO41DiIiIiEilo06wiIiIiJ1RJ9h26gSLiIiISKWjTrCIiIiInVEn2HYqgkVERETsjIpg22kcQkREREQqHXWCRUREROyMOsG2UydYRERERCoddYJFRERE7Iw6wbZTESwiIiJiZ1QE207jECIiIiJS6agTLCIiImJn1Am2nTrBFeDo0aM0btzY/JiQkABAaGgo8fHxFZxd%2BVq/fj0nTpyo6DRERET%2BVgoLy/%2BrslERXMG2bt1KcHBwRadhiPT0dEaMGEF2dnZFpyIiIiJiQeMQFczX17eiUzCMyWSq6BRERET%2Blipj57a8qRNcwS4fh7hcUlISwcHBrFq1CoCzZ88yZswYWrZsSUhICLGxseTk5Fi9n7Vr19KlSxeCgoLo168fv/76q3ldfHw8Xbt2pXnz5vTu3Zvt27eb1/15RCMhIYHGjRsD/xvr2LRpE507d6ZZs2YMGTKE06dPA3D//febH/9uYx4iIiJi39QJvgEdOnSIIUOGMGzYMPr06QNATEwMly5d4sMPPyQ3N5epU6cyZcoUXn311WvG27JlCzExMcTExNChQweWLVvGkCFD%2BOqrr1i3bh2xsbFMnjyZ5s2bEx8fz%2BDBg9mwYQP%2B/v5W5btw4ULmzJmDyWTiueeeY8mSJYwcOZKVK1fy6KOPsnLlSm677TarX39mZiZZWVkWy6pV88XPz8/qGNZycLB8lD8x6sDowF%2BVs7Mx/QknJ0eLR0MYFdrR0fLRKEaMp91%2Bu%2BWjUaz8P7vMata0fDRCo0bGxL3lFsvH8rZ/vzFxraBOsO1UBN9gjh8/ztNPP80//vEPBg4cCMCRI0fYvHkz27Zto2rVqgDExsYSHh5OdHS0edmVrFixgh49etC/f38AXnrpJVxcXDhz5gzLli0jIiKC8PBwAEaPHs327dtZvnw5o0aNsirn4cOH07x5cwB69uzJzz//DECNGjXMj%2B7u7lYfgxUrVhAXF2exbOjQKIYPH2Z1jLJycTEsNGBocKOTN5Y95%2B7kZFhoHx9Pw2IDeHt7GBrfUB4G575jh3GxP/jAuNjXw8MPGxf7/9/vDDNhgjFx77vPmLhWUBFsOxXBN5h58%2BaRn5/PTTfdZF6WkpJCYWEhHTt2tNi2sLCQw4cPc%2Bedd1415qFDh%2BjXr5/5e1dXV8aOHWuOPXToUIvtW7RoQUpKitU5BwQEmP/s5eXFpUuXrH5uafr27UtoaKjFsmrVfMnLsylsqRwciuqwS5fAqBFmVwfbjsdVFSdvFKMOyvU48AYWqTg5QUGBYeFPnbV%2B1KksnJwc8fb24OzZbAoKjHkH9XE1JGxRB9jDA7KzjX33v%2Bee8o95%2B%2B1FBfBjj0FycvnHLxYVZUzcmjWLCuC1a8Goq/38%2BKMxcW%2B5pagAnjoVjhwxZh9it1QE32A6depE27Zt%2Bde//kWXLl2oUaMGBQUFVK1alU8%2B%2BaTE9taMLDg7X/mv2c3NrcSygoICCq/wJlNQyhu/Szl38/z8/EqMPuTmGlcrQVFsw%2BLb82/8jT650dADb7/y841t8RQUFBq3D6PfVYy%2BltPOncbFTk42Nn5GhnGxoagANmofRo8VHDlSoaMLRlAn2HY6Me4GExoayuOPP46/vz8zZ84EIDAwkHPnzuHg4EBAQAABAQHk5OQwY8YM8qxojwYEBJB8WfehoKCA0NBQEhMTCQwMJCkpyWL7pKQkAgMDgaIC98KFC%2BZ1aWlpVr8WB817ioiIyA1KRfANyMnJiQkTJrB69Wp27txJw4YNueeeexg9ejS7d%2B9mz549REdHc/HiRby9va8ZLyIigk8//ZTVq1dz%2BPBhpk%2BfjslkomnTpkRGRrJ8%2BXLWrFnDoUOHmDVrFsnJyeYT8po1a8aqVav47bffSEhIYPHixVa/Do//n91LTk62KKRFRETENrpZhu1UBN%2Bg2rVrx4MPPsiUKVMoKChgxowZ1K1bl8jISAYMGEBgYCBz5syxKlabNm2YPHkyCxYsICwsjL1797Jw4ULc3d3p1q0bI0eOZN68eYSFhbFt2zYWL15Mw4YNARgxYgTe3t707t2badOm8cILL1j9GmrUqEFYWBgjRoxg5cqVf%2Bk4iIiISEkqgm3nYNIdDcQO5OYaE9fBAVxdIS/PuNFUN0c7PjHOqP8Vr8eBv8osvM0MPjEu6%2BRFQ%2BI6Ozvi4%2BPJqVMXDJsJ9rX%2BQjBl4%2BgInp5w4YKx79ZW/HatzIKDi6460bKlsTPBVlwy8y/x9y%2B6esPixcbNBG/aZEzcRo1g0SIYPNi4meBvvjEm7jVMn17%2BMaOjyz/m5UwmE7Nnz2bVqlUUFhbSp08fRo8ejeMVLn04depUli1bZrFs4sSJPPHEEwCsW7eOf/3rX2RlZZnvoVB8ZSpr6MQ4ERERETtjj53bJUuWsG7dOuLi4sjPz2fMmDHUrFmTQYMGlbp9SkoKo0aNolevXuZlXl5eAOzevZuYmBheeeUVbr/9dqZNm0Z0dDRvvvmm1fmoCLZzGzduZNy4cVdc36pVK95%2B%2B%2B3rmJGIiIhISUuXLmX48OG0bt0aKLo3wdy5c69aBA8aNAhfX98S65YvX07Xrl3N9zmYMWMG9913H2lpadSrV8%2BqfFQE27mQkBDWrFlzxfVluUmFiIiI2Ad76wRnZGRw7Ngx2rRpY17WqlUr0tPTyczMLHFp1PPnz5ORkUH9%2BvVLjZeUlMQzzzxj/r527drUqVOHpKQkFcGVhaenJ56ext5dSkRERG4s9lYEZ2VlAVgUu7Vq1QLgjz/%2BKFEEp6Sk4ODgwMKFC/nvf/9L9erVGTBggHk0orTCuWbNmvzxxx9W56QiWERERETIzMw0F6vFfH19SxSbV5KTk0PGFU6evHix6IRfV9f/3Vay%2BM%2Bl3fPg4MGDODg40KBBA5544gm2b9/OxIkT8fLy4oEHHiAnJ8ciVnE8a%2B6fUExFsIiIiIidMaITvGLFCuLi4iyWRUVFMWzYMKuen5SUxJNPPlnqujFjxgBFBW/x3WqLC9bi%2BwpcLjw8nPvuu4/q1asDcPvtt5OamsqHH37IAw88gJubW4mCNy8vr9RYV6IiWERERETo27cvoaGhFstKOyntStq1a8e%2BfftKXZeRkcHMmTPJysqibt26wP9GJErbh4ODg7kALtagQQN%2B/PFHAPz9/Tl%2B/LjF%2BuPHj5cpXxXBIiIiInbGiE6wn5%2Bf1aMPZeXv70%2BdOnVITEw0F8GJiYnUqVOn1H3OnTuXnTt38u6775qXJScn06BBAwCCgoJITEykd%2B/eABw7doxjx44RFBRkdU4qgkVERETsjL2dGAfQv39/Zs2axU033QTA7NmzGThwoHn9yZMncXNzw9PTk/vuu49Fixbxzjvv8MADD7B161bWrFnD0qVLzbEiIiJo0aIFzZo1Y9q0aXTq1MnqK0OAimARERERuQ4GDRrEiRMniIqKwsnJiT59%2BhAZGWle36dPH3r16sWwYcNo3rw5c%2BfOZd68ecydO5ebb76Z2bNnExwcDEBwcDBTpkxh3rx5nDlzhrvvvpvY2Ngy5aMiWERERMTO2GMn2MnJiejoaKKvcH/mr7/%2B2uL7zp0707lz5yvG6927t3kc4q8o/WbNIiIiIiJ/Y%2BoEi4iIiNgZe%2BwE32hUBIuIiIjYGRXBttM4hIiIiIhUOuoEi4iIiNgZdYJtpyJY7IKjwb%2BzcHAo%2BjJEfr4xcR0cwMUFCgrAZDJmH0YpPtiFhcblbuRxd3Kyz%2BN%2BPTgb9LZS/DPj5GTsfwivvlr%2BMf39ix6joiAjo/zjFxs/3pi4wcEwcCDExcHOncbsIz7emLjVqhU99usHZ84Ysw%2BxWyqCRUREROyMOsG2UxEsIiIiYmdUBNtOJ8aJiIiISKWjTrCIiIiInVEn2HbqBIuIiIhIpaNOsIiIiIidUSfYdiqCRUREROyMimDbaRxCRERERCoddYJFRERE7Iw6wbZTJ1hEREREKh11gkVERETsjDrBtlMRLCIiImJnVATbTuMQIiIiIlLpqBMsIiIiYmfUCbadOsEiIiIiUumoEywiIiJiZ9QJtp2KYBERERE7oyLYdhqHEBEREZFKR0WwjebPn09ERATx8fGEhoZec/vLt0tISKBx48YAHD16lMaNG3P06FFD872e8vLy%2BPjjjys6DRERkb%2BdwsLy/6psVASXk27durFq1aoyPSc4OJitW7calFHF%2B/zzz1m4cGFFpyEiIvK3oyLYdpoJLifu7u64u7uX6Tmurq74%2BvoalFHFM5lMFZ2CiIiISKnUCS6jAwcO0L9/f4KCgnjyySc5deoUQIlxiDlz5hASEkLz5s2JiIhg//79JWJdPg7xZ8uWLaN169bs3bsXgN9%2B%2B42IiAiaN2/OQw89xPvvv291zvn5%2BeZ8WrVqxfDhw8155%2BbmMnPmTO69915atGjBs88%2By7Fjx4DSRzSKxz%2BKX3NERATz5s2jXbt2tG7dmunTp2MymUhISCA6Opr09PS/3ZiHiIhIRVMn2HbqBJdBXl4egwcPpnXr1kydOpUff/yRV199lZYtW1ps9%2BWXX7JixQoWLFiAn58fr7/%2BOtHR0VaPS2zYsIE5c%2Bbw1ltv0aRJE3JycnjmmWfo1asXsbGxHDx4kIkTJ%2BLp6Ul4ePg1482dO5c1a9bw6quvUqdOHSZPnszkyZOZN28ekydPZseOHfzzn/%2BkevXqzJo1i%2Beff55PPvnEqlx37txJrVq1%2BPDDD/n5558ZN24cHTt2pE2bNowfP57FixezatUqatSoYVU8gMzMTLKysiyW%2Bfj44uvrZ3WMG4qDg7FxjYpvpOuRux0fd2dnY/oTTk6OFo%2BGsOPjDoC/f/nHrFnT8tEowcHGxL39dstHI1SrZkxcLy/Lx/J25owxceW6UBFcBt9//z2nT5/m5ZdfpkqVKjRs2JBt27Zx8uRJi%2B3S09NxcXGhTp061KlTh4kTJ3Lw4EGr9vHTTz/xyiuv8Prrr9O6dWsAPvvsM2rWrMmIESMAqF%2B/Punp6SxduvSaRbDJZOLjjz9m7NixdOzYEYBXXnmF9evXc%2BbMGdauXctbb73FXXfdBcCsWbPo1KkT3333HYGBgdfMt6CggNjYWLy8vGjQoAHvvvsuP//8M3fffTdVq1bFycmpzCMfK1asIC4uzmLZ0KFRDB8%2BrExxysLFxbDQ4FK2MZkyc3MzNr6R7Dl3A39ofFxdDYsN4O3tYWh8Qxn9MzNwoHGxH37YuNhgbO4AH3xgbHwjtW1rTNzVq42Ja4XK2LktbyqCy%2BDAgQPUr1%2BfKlWqmJc1a9aMb7/91mK77t27s3z5cu6//35atGhB586d6dOnj1X7mDRpEgUFBdSuXdu87ODBgyQnJxN82af8goICnJycrhnv1KlTnD59mqZNm5qX3XrrrQwbNoykpCQKCwsJCgoyr6tevTqBgYGkpKRYVQTXrFkTr8s%2BYXt5eZGfn3/N511N3759S1xpw8fHl0uXbAp7RS4uGBYbwKUgx5jADg5FBUFuLtjb/PX1yN3RoG6ng8P/fmgMyv3UBWN%2BIJ2cHPH29uDs2WwKCox5B/XxuPb/S3/J9fp5N6LQq1mzqABeuxZOnCj/%2BMX%2B1DwoN7ffXnRcHnsMkpON2cesWcbE9fIqKoC3bYPz543ZRwVREWw7FcFl9OeTvVxK6Qb5%2Bvqyfv16vvvuO7755hveeecdPv74Y9asWXPN%2BC%2B%2B%2BCI7duxgypQp5rnf/Px82rdvz6RJk8qcr7Pzlf%2BK3a7QUSkoKKCwsBCHUn7t%2BOcC17WUjpWtJ8T5%2Bfnh52c5%2BmBkkWo4owtUk8n%2BiuBiRuZux8c9P9/Yd7eCgkLj9mEy%2BFQTo3/eMzKMi33ihLHxd%2B40LjYUFcBG7cPosYLz5zW6ICXoxLgyaNSoEampqZw7d868rPjEtcv95z//YeXKlXTq1IlXXnmFtWvXkpqaym%2B//XbNfXTu3JmxY8fyyy%2B/mIvmwMBADh06RN26dQkICCAgIIBdu3axbNmya8bz9vbGx8eH5Ms%2Bve/du5eOHTtSt25dnJ2d2bVrl3ndqVOnOHz4MIGBgeYC/8KFC%2Bb1ZTnBrbQiWkRERGynE%2BNspyK4DDp06EDt2rWJiYkhJSWF%2BPh4vvjiixLbFRYWMmPGDL788kuOHj1KfHw8Hh4e1K9f36r93HzzzTz99NPMnDmTc%2BfOERYWRk5ODpMmTSIlJYVvv/2WadOmUdPKkywiIiKYO3cuP/74I/v372fatGm0aNECLy8vHn30UWJjY0lISCA5OZkxY8Zw0003cffdd1OrVi1q167NO%2B%2B8Q1paGvHx8fznP/%2Bx%2Bnh5eHhw5swZUlNTbR6REBERESlPKoLLwMXFhTfffJMzZ87Qq1cvPvzwQx5//PES24WGhjJ8%2BHCmT59O165d%2BeKLL/j3v/9NtTKc/frMM8/g6urK3Llz8fLy4q233iI1NZXw8HAmTJjA448/zpAhQ6yKNXjwYB588EFGjBhB//79uemmm4iNjQVg7NixdOjQgeHDh9O/f3/c3Nx49913cXV1xdHRkWnTprF79266devGhg0bePbZZ61%2BDXfddRcBAQH07Nmz1I65iIiI/DXqBNvOwaQ7GogdMPTENaNPjMvPNiawgwO4u0NOjv3NBF%2BP3I08Mc7VFfLyDMs962yeIXGdnR3x8fHk1KkLhs0E%2B3oZdKrJ9fp5/9e/yj%2Bmv3/RlRsWLzZ2Jnj8eGPiBgfDjh3QsqVxM8Hx8cbErVYNQkPh66%2BNmwnu1cuYuNfQs2f5x/zss/KPeSNTJ1hEREREKh1dHcLOTZs27ao34RgyZEiZRhhERETkxlcZxxfKm4pgO/fcc8/xxBNPXHF9WeaQRURERCoLFcF2rkaNGmW6JbGIiIjYP3WCbaciWERERMTOqAi2nYpgERERETGcyWRi9uzZrFq1isLCQvr06cPo0aNxLOVqPuPGjWP16tUllrdr146lS5cC0Lp1a4sbmAHs2LEDT09Pq/JRESwiIiJiZ%2ByxE7xkyRLWrVtHXFwc%2Bfn5jBkzhpo1azJo0KAS28bExDBq1Cjz9%2Bnp6URERPDkk08CkJGRwblz59i8eTPu7u7m7apUqWJ1PiqCRURERMRwS5cuZfjw4bRu3RqA0aNHM3fu3FKL4KpVq1K1alXz9%2BPGjaNLly507twZgJSUFHx9falXr95fzkdFsIiIiIidsbdOcEZGBseOHaNNmzbmZa1atSI9PZ3MzEz8/Pyu%2BNwffviB7du3s3HjRvOyAwcOEBgYaFNOKoJFRERE7IwRRXBmZiZZWVkWy3x9fa9aoFqrOO7lsWrVqgXAH3/8cdV9LFq0iF69elG7dm3zspSUFLKzs4mIiODQoUM0adKE8ePHl6kwVhEsIiIiIqxYsYK4uDiLZVFRUQwbNsyq5%2Bfk5JBxhVuDX7x4EQBXV1fzsuI/5%2BVd%2BVbxaWlp/Pjjj8TExFgsP3jwIGfOnOHFF1/Ey8uLt956i8jISD7//HO8vLysyldFsIiIiIidMaIT3LdvX0JDQy2W%2Bfr6Wv38pKQk84lrfzZmzBigqOB1c3Mz/xnAw8PjijE3btxIkyZNuPXWWy2Wv/POO1y6dMl8JYhZs2Zx77338s0339CzZ0%2Br8lURLCIiIiL4%2BfnZNPrQrl079u3bV%2Bq6jIwMZs6cSVZWFnXr1gX%2BNyJxtUJ7y5Yt3H///SWWu7q6WnSV3dzcqFu37hU70aUpeWE2EREREbmhFRaW/5eR/P39qVOnDomJieZliYmJ1KlT54qFt8lk4ueff6Zly5Yllnfu3Jn4%2BHjzsosXL3L48GEaNGhgdU7qBIuIiIjYGXu7OgRA//79mTVrFjfddBMAs2fPZuDAgeb1J0%2BexM3NzTzikJ6ezoULF0qMQjg4ONCpUyfmz5/PzTffTI0aNZg7dy433XQT9957r9X5qAgWEREREcMNGjSIEydOEBUVhZOTE3369CEyMtK8vk%2BfPvTq1ct8It6JEycAqFatWolYY8aMwdnZmVGjRnH%2B/HnuuusuFi1ahJOTk9X5OJhMJpNtL0nEeLm5xsR1cABXV8jLA6P%2BJbhhx8mXcivLcuPiApcuGRffSAbnnnU6x5C4zs6O%2BPh4curUBfLzjWkj%2BVYx6GfG0RE8PCA729gWWI8e5R%2BzUSNYtAgGD4b9%2B8s/frHhw42JW60ahIbC11/DmTPG7KN3b2PiBgfDjh3QsiXs3GnMPiqojGrfvvxj/vBD%2Bce8kWkmWEREREQqHY1DiIiIiNgZe5wJvtGoCBYRERGxMyqCbadxCBERERGpdNQJFhEREbEz6gTbTp1gEREREal01AkWERERsTPqBNtORbCIiIiInVERbDuNQ4iIiIhIpaNOsIiIiIidUSfYduoEi4iIiEilo06wiIiIiJ1RJ9h2KoJFRERE7IyKYNtpHEJEREREKh11gkVERETsjDrBtlMRLCIiImJnVATbTuMQIiIiIlLpqBMsIiIiYmfUCbadOsGl%2BOqrr%2BjYsSNBQUFs2bLluu23cePGJCQkAHDixAnWr19/3fZtlPXr13PixImKTkNERETEgorgUsybN4%2BQkBC%2B%2BOIL2rRpc932u3XrVoKDgwGYNWsW33777XXbtxHS09MZMWIE2dnZFZ2KiIjI30phYfl/VTYahyjFuXPnaNWqFTfffPN13a%2Bvr6/5zyaT6bru2wh/h9cgIiJyI6qMRWt5Uyf4T0JDQ0lPT2f8%2BPGEhoaSmJhI//79CQoKokWLFjzzzDNkZmZSWFjIPffcwyeffGJ%2BrslkomPHjqxduxaAnTt30r9/f1q0aEFoaCgffvihedtx48Yxbtw4wsLCaN%2B%2BPampqeZxiPnz57N69WpWr15NaGgoAGfPnmXMmDG0bNmSkJAQYmNjycnJsfp1rV27li5duhAUFES/fv349ddfzevi4%2BPp2rUrzZs3p3fv3mzfvt3ieMTHx5u/T0hIoHHjxgAcPXqUxo0bs2nTJjp37kyzZs0YMmQIp0%2BfBuD%2B%2B%2B83P14eQ0RERKSiqRP8J6tWraJXr14MHDiQzp078/DDDxMZGcmMGTPIzMxk/PjxLFq0iAkTJtClSxe%2B/PJLHnnkEQB27drF6dOnuf/%2B%2B0lJSeGpp54iMjKSadOmkZSUxCuvvEKtWrV44IEHgKLCdMGCBdSqVYv69eubcxg4cCApKSkATJo0CYCYmBguXbrEhx9%2BSG5uLlOnTmXKlCm8%2Buqr13xNW7ZsISYmhpiYGDp06MCyZcsYMmQIX331FevWrSM2NpbJkyfTvHlz4uPjGTx4MBs2bMDf39%2BqY7Zw4ULmzJmDyWTiueeeY8mSJYwcOZKVK1fy6KOPsnLlSm677Tar/w4yMzPJysqyWFatmi9%2Bfn5Wx7CWg4PlozEMCn59kpcK4OxsTH/CycnR4tEQjgbFvvzn3ah9ADRqVP4xb7nF8tEo1aoZE9fLy/LRCP8/Cljubr/d8rG87dxpTFwrqBNsOxXBf1KjRg2cnJyoWrUqrq6uPP/88wwYMAAHBwfq1avHgw8%2ByO7duwHo3r07ERERnD9/Hi8vLzZu3Mi9996Ll5cX8%2BfP54477uDFF18EoEGDBqSkpPD222%2Bbi%2BBmzZqZO72X8/T0xN3d3ZzPkSNH2Lx5M9u2baNq1aoAxMbGEh4eTnR0tHnZlaxYsYIePXrQv39/AF566SVcXFw4c%2BYMy5YtIyIigvDwcABGjx7N9u3bWb58OaNGjbLqmA0fPpzmzZsD0LNnT37%2B%2BWdz7sWPxa/HGitWrCAuLs5i2dChUQwfPszqGGXl4mJYaMDVyOBGJ28s5V4qHx9jj4u3t4eh8Q1Vhv9L/pJFi4yLPWGCcbGvh7ZtjYu9Y4dxsQE%2B%2BMCYuGpC2DUVwVfh6%2BtLeHg47777Lnv37uXAgQPs27ePli1bAtCiRQt8fX359ttv6d69O5s2bWLMmDEApKSkmAvDYsHBwXz00Ufm762dOU5JSaGwsJCOHTtaLC8sLOTw4cPceeedV33%2BoUOH6Nevn/l7V1dXxo4da449dOhQi%2B1btGhh7kRbIyAgwPxnLy8vLl26ZPVzS9O3b98SHw6qVfMlL8%2BmsKVycCiqZS5dAqNGmF0xIHG4Pskb%2BR98ce72yODcT5035mfGyckRb28Pzp7NpqDAmDaSj7uBnWB3d8jJMe7nHeCFF8o/5i23FBXAU6fCkSPlH7/YZf/Plysvr6ICeNs2OH/emH2MHm1M3NtvLyqAH3sMkpON2UcFUSfYdiqCryIjI4NHHnmEpk2b0qFDB/7xj3/wn//8h6SkJPM23bp1Y%2BPGjQQEBHDq1Ck6deoEgJubW4l4hYWFFBQUmL8vbZvSFBQUULVqVYv542JTOwfwAAAgAElEQVTWjCw4O1/5r7m0HAoKCii8wr%2Buy/Mv5lLOHTE/P78Sow%2B5uca%2B75lMRsY3%2BARBI5NXl6NC5Ocb%2B%2B5WUFBo3D6MSr14BMJkMvbdf/9%2B42IfOWJs/DNnjIsNRQWwUfsweqwgOblCRxeMoCLYdjox7iq%2B/PJLqlWrxptvvslTTz1F69atSUtLs7jqQffu3fnuu%2B/YuHEjoaGheHgU/ZoxMDDQoliGohPlAgMDrdq3w2XFR2BgIOfOncPBwYGAgAACAgLIyclhxowZ5FnRHg0ICCD5sk/ABQUF5pP%2BSsszKSnJnKeLiwsXLlwwr0tLS7Mq/z%2B/BhEREZEbiYrgq6hevTq///47P/zwA2lpaSxatIhNmzZZFJ5NmjTBz8%2BP5cuX07VrV/Pyxx57jL179zJnzhwOHTrE6tWr%2BeCDD3j88cet2reHhwfp6elkZGTQsGFD7rnnHkaPHs3u3bvZs2cP0dHRXLx4EW9v72vGioiI4NNPP2X16tUcPnyY6dOnYzKZaNq0KZGRkSxfvpw1a9Zw6NAhZs2aRXJyMn369AGK5pZXrVrFb7/9RkJCAosXL7b6%2BBV/IEhOTrYopEVERMQ2uk6w7VQEX0XXrl0JCwtj%2BPDhPPLIIyQkJDB27FhSUlIsCuFu3brh5ORkMbNbp04d3nzzTbZs2ULPnj154403GDdunPlKEtfy8MMPc%2BjQIcLCwjCZTMyYMYO6desSGRnJgAEDCAwMZM6cOVbFatOmDZMnT2bBggWEhYWxd%2B9eFi5ciLu7O926dWPkyJHMmzePsLAwtm3bxuLFi2nYsCEAI0aMwNvbm969ezNt2jReKMO8XI0aNQgLC2PEiBGsXLnS6ueJiIiIGM3BpDsaiB3IzTUmroMDuLpCXp5xY7Vu2HHyRl6KSifGXVHWaeuvAV4Wzs6O%2BPh4curUBcNmgn2rGPQz4%2BgIHh6QnW1sy6pHj/KP2ahR0VUnBg82diZ4%2BHBj4larBqGh8PXXxs0E9%2B5tTNzg4KIrT7RsadxMcAWVUZfdX6vc/OnqpH97OjFORERExM5UxvGF8qYi2M5t3LiRcePGXXF9q1atePvtt69jRiIiIiI3PhXBdi4kJIQ1a9ZccX1ZblIhIiIi9kGdYNupCLZznp6eeHp6VnQaIiIiInZFRbCIiIiInVEn2HYqgkVERETsjIpg2%2Bk6wSIiIiJS6agTLCIiImJn1Am2nTrBIiIiIlLpqBMsIiIiYmfUCbadimARERERO6Mi2HYahxARERGR68ZkMjFw4EDi4%2BOvul1aWhqRkZG0aNGCbt26sXXrVov133//PT169CAoKIgnn3yStLS0MuWhIlhERETEzhQWlv/X9cm7kKlTp/Ldd99ddTuTycTQoUOpVasWn3zyCQ8//DBRUVH8/vvvAPz%2B%2B%2B8MHTqU3r17s2rVKmrUqMHzzz%2BPyWSyOhcVwSIiIiJiuIyMDJ566im%2B/vprvL29r7rtjz/%2BSFpaGlOmTKFhw4YMGTKEFi1a8MknnwCwcuVK7rzzTgYOHEijRo2YPn066enpbNu2zep8VASLiIiI2Bl77ATv2bOH2rVr88knn1C1atWrbpuUlMQdd9xBlSpVzMtatWrFrl27zOtbt25tXufh4UHTpk3N662hE%2BNERERE7IwRRWtmZiZZWVkWy3x9ffHz8yuX%2BKGhoYSGhlq1bVZWVon91qxZkz/%2B%2BMOq9dZQESx2wc3NmLiZmZmsWLGCvn37lts/8pKMSf765G4M5X51vr4uhsTNzMxk/vzF9nvc337b%2BNy/%2BabcQ2ZmZrJi/nz6Tp1qd8cdLsvfyGNfhjnOsjDnvmGDXR77qzHikM2fv4K4uDiLZVFRUQwbNsyq5%2Bfk5JCRkVHqOl9fX4uu7rVkZ2fj6upqsczV1ZW8vDyr1ltD4xBSqWVlZREXF1fik689UO4VQ7lXDOVecew5f3vOvSL07duX%2BPh4i6%2B%2Bffta/fykpCQefPDBUr%2B%2B//77MuXi5uZWoqDNy8vD3d39qus9PDys3oc6wSIiIiKCn5%2BfTR3zdu3asW/fvnLJxd/fnwMHDlgsO378uDk/f39/jh8/XmJ9kyZNrN6HOsEiIiIickMJCgpiz5495OTkmJclJiYSFBRkXp%2BYmGhel52dza%2B//mpebw0VwSIiIiJS4U6ePMmFCxcAaNu2LbVr1yY6Opr9%2B/ezaNEidu/eTZ8%2BfQB45JFH2LFjB4sWLWL//v1ER0dTt25d2rVrZ/X%2BnF5%2B%2BeWXjXghIvbC09OTtm3b4unpWdGplJlyrxjKvWIo94pjz/nbc%2B5/Z%2B%2B99x5t27a1GF/o3r07OTk5tGvXDkdHR0JCQvjss8%2BYN28eGRkZvPbaa9xxxx0AeHt7c8cdd7Bo0SIWL16Mj48Ps2bNolq1albn4GAqy601RERERET%2BBjQOISIiIiKVjopgEREREal0VASLiIiISKWjIlhEREREKh0VwSIiIiJS6agIFhEREZFKR0WwiIiIiFQ6KoJFREREpNJRESwiIiIilY6KYBERERGpdFQEi9iJJ598krNnz5ZYfvLkSXr37l0BGZWNPecfFxdHdnZ2ieXnz5/ntddeq4CMrGfPx33NmjXk5eWVWH7x4kWWLl1aARlZz55z//333zGZTCWWFxQUsGfPngrIyHr2fNzl%2BnOu6ARErof/a%2B9Oo5q8tj6A/6MM4oQiimBRI1ZxAEGkqIggYBVwqtbWCUWrXPU6v06g0jKIdWixjmAVWnColUrFDoJiRUUFFOeKCkixcLVWVAYHMOT94CKLkBCCLTnPCfv35ZYnuWv9e24u7JznPHu7urpCJBKp9d6kpKR6TqO%2B06dP49q1awCA9PR0hIeHo2nTpnLv%2BeOPP5Cfn88iXq14zp%2BTk4PHjx8DALZv3w5LS0sYGhrKvefOnTv47rvvsHLlShYRa8TzuhcWFuLly5cAAD8/P7z77rto3bq13Htu3bqFTZs2YerUqSwi1ojn7FW5ubkhJSUFRkZGctf//PNPTJo0CVevXmWUTDltWXeieVQEkwZh/vz5sn/Oy8vDt99%2Bi4kTJ8LKygq6urr4/fffsXfvXkybNo1hSkVisRi7d%2B%2BGVCqFVCpFRkYGdHV1Za%2BLRCI0bdoUa9euZZiyZjzn/%2Buvv%2BDj4yP7ed68eQrvMTAwENxnBuB73dPS0rBo0SLZl9YPP/xQ7vXKHcpRo0ZpPFtteM5%2B6NAhhIeHA3iTc9y4cWjUSP5mcVFRESwsLFjEU4nndSdsiaTK7nkQosXGjh2LWbNmwcPDQ%2B76iRMnsHnzZvz000%2BMkqnm5%2BeHVatWoXnz5qyjvBWe87u6uiI2NlZhZ4wHPK57QUEBKioq4O7ujkOHDsmtu0gkgoGBgcJOn1Dwmr28vBw///wzKioq4O/vD39/f7Ro0UL2emX2/v37K9wREQJe152wRUUwaXBsbW0RGxursKNx%2B/ZtTJgwAZcvX2aUTD2PHj3C69evFc7smZmZMUpUN7zn5xWtO1FXWloa%2BvbtCx0dullMtBsVwaTBmTlzJqRSKUJDQ2FiYgIAuH//Pvz9/dGqVSts3bqVcULlUlJSsGbNGvzvf/8D8OYWn0gkkv3nrVu3GCdUjef8%2Bfn52Lx5M65fv660kBTSOfLqeF73oqIiREZG1rjuQn7QiefsAHD%2B/Hlcv34d5eXlCtmVHQ0SCt7XnWgWfc0jDU5oaCgWLFgAZ2dntGrVClKpFM%2BePcOAAQMQEhLCOl6NgoKCYG1tjZ07d3J1a7sSz/mXL1%2BOJ0%2BeYPLkydxl533dr1%2B/jpEjR1J2Dfr8888RHR0NS0tLNGvWTO41dR8wZoXndSeaRzvBpEEoKCiAqakpRCIRCgoKALxpmZOcnAyRSAQXFxc0adIEgHBvD/fp0wc//fQTzM3NWUd5Kzznt7KyQlxcHLp27co6Sp3xvO7W1tbYu3cvrK2tWUepM56z29vbY82aNVw%2BSMbzuhPNo51g0iC4uroiJSUFbdq0kWuXVvkdcOPGjYK/PdyvXz9cunSJy2IG4Dt/586dUVhYyDrGW%2BF53U1MTBQ6FPCC5%2ByNGzfmtojked2J5tFOMGkQ8vPzYWZmBpFIVGtv1A4dOmgoVd2Eh4cjIiICzs7O6NSpk1zLK0DY5/QAvvPHx8dj27ZtmD59utLs9vb2jJLVjud1P378OCIiIrBgwQKl2YV61wbgO/vWrVuRm5uL4OBghf7SQsfzuhPNoyKYEE54e3vX%2BJpIJBL8Ax8857e0tKzxNSHfPQC0Z92rnkUV%2Bl0bgO/s3t7euHz5MqRSKdq0aaNQSAr5QVCe151oHhXBhBBCBInXuzYA39nj4uJUvv7BBx9oKEnd8bzuRPOoCCaEEz/%2B%2BKPK18eMGaOhJG%2BH5/yVD1PWRMi3WHled0IIqU9UBBPCCVdXV7mfJRIJHj9%2BDB0dHVhbWwv6tjbAd35LS0tZb11AsU2UkG%2BxasO610TI685zdm9vb5XZ6TNDtAV1hyCEEydPnlS4VlpaioCAAHTv3p1BorrhOX/1M5ASiQR5eXnYunUr5s6dyyiVenhe9%2BrFVuW6R0VFYdGiRYxSqYfn7A4ODnI/v379Gvfv30dycjLmzJnDKJV6eF53onm0E0wI53JzczFx4kScP3%2BedZS3wnP%2Ba9euYdmyZUhISGAdpc54XvfU1FSsW7eu1qMeQsRz9sOHDyMxMRHh4eGso9QZz%2BtO6g810yOEc5mZmaioqGAd463xnF8kEuHhw4esY7wVntfdyMgIOTk5rGO8FZ6z29vbc/mlCeB73Un9oeMQhHBC2Tm90tJS3L59Gz4%2BPmxC1QHP%2Bbdt26ZwrbS0FMeOHYOjoyODROrjed2V7dqVlpYiNjYWNjY2DBKpj%2Bfsyh4ELS0txZ49ewTfXYHndSeaR8chCOGEskJMT08PVlZWGDBgAINEdcNz/uq9dkUiEXR1dWFlZYXp06fD0NCQUbLa8bzu1R/qq7ruixYtEnRBxnP26g%2BCVjI1NUVoaKigPzc8rzvRPCqCCeFQSUkJJBKJoIsvVXjPzytad6KO6r12KwtJY2NjlZ0XCOENFcGEcOTbb7/F7t278ffffwN4c85t4sSJgh59WxXP%2BX///Xfs2bMHOTk5kEgkEIvFmDx5Mt577z3W0WrF87r/9ddf2LdvH7KzsyGRSNClSxeMHz8enTt3Zh2tVjxnB4CUlBRkZ2ejoqICYrEYAwcOVJgeJ0S8rzvRHDoTTAgntm/fjr1792LhwoWwtbVFRUUFMjIysG3bNujp6cHX15d1RJV4zn/8%2BHEsXrwY77//PsaOHQuJRIIrV65gxowZ2Lx5M9zd3VlHrBHP637x4kXMmjUL3bt3h42NDSQSCdLT07F3715ERkbCzs6OdcQa8Zz9wYMHmDt3Lu7duwexWAyJRII//vgDZmZmiIqKgomJCeuINeJ53QkDUkIIFwYPHixNSkpSuH78%2BHGpi4sLg0R1w3N%2BLy8vaVRUlML1qKgo6ahRozQfqA54Xvdx48ZJN23apHB948aN0o8//phBIvXxnH327NnSmTNnSp8%2BfSq7VlhYKP3kk0%2Bk8%2BfPZ5isdjyvO9E8apFGCCdKSkqU3s4Ti8UoLCzUfKA64jn//fv3MWTIEIXrQ4YMwb179xgkUh/P63737l2MGzdO4fqHH34o%2BMlfPGe/cOECli1bJnd2vHXr1li6dClSUlIYJqsdz%2BtONI%2BKYEI4YWtri8jISLnerhKJBJGRkbC2tmaYTD0857ewsMDp06cVricnJwv%2BaXOe171Dhw64du2awvWrV6/C2NiYQSL18Zzd0NAQz549U7heVFQk%2BDPBPK870Tw6E0wIJ/z8/DB58mScO3cOvXr1AgDcuHEDZWVl2LNnD%2BN0teM5//z58zF//nxcvXoVffr0AQBcuXIFCQkJ2LBhA%2BN0qvG87jNnzsSnn36KnJwcWcF%2B9epVxMTEYMmSJYzTqcZzdi8vL6xevRqfffYZrKysALzJHhQUBE9PT8bpVON53YnmUXcIQjjy5MkT/PTTT8jOzoa%2Bvj7EYjFGjhyJZs2asY6mFp7znz9/Hvv375fL7uPjI/jdVIDvdT98%2BDD27t2rsO4eHh6so9WK1%2BxlZWUICAhAfHy8rFdw48aNMX78eKxYsQJNmjRhnFA1XtedaB4VwYRwJDk5GY0aNYKTkxMAYO3atXBycsLgwYMZJ1MPz/kfP36MoqIiiMViAMAvv/wCe3t7tG3blnGy2vG87q9fv8bTp09lt7IvX76MXr16QU9Pj3Gy2vGcHXhz/CE3Nxd6enro2LEjmjZtyjqSWnhfd6I5dCaYEE7ExMRg8eLFsl6vAKCjo4NFixbh%2B%2B%2B/Z5hMPTznP3/%2BPIYOHYqjR4/KrkVHR8PT0xOXLl1imKx2PK/7rVu34ObmhsjISNm1pUuXYvjw4bh79y7DZLXjOfvTp0%2BxcOFCREdHw9raGpaWlvDw8MDixYtRXFzMOp5KPK87YYBpbwpCiNqGDBkiPXnypML1EydOSN3c3Bgkqhue848ePVoaERGhcD08PFw6duxYBonUx/O6T5gwQRocHCwtKyuTXZNIJNLg4GDplClTGCarHc/ZFyxYIJ08ebI0KytLdu3333%2BXTpkyRbp8%2BXKGyWrH87oTzaOdYEI48eTJE3Ts2FHhulgsltvlEyqe8%2Bfm5mL48OEK1z08PJCVlcUgkfp4Xvdbt25h2rRpch0JGjVqhKlTp%2BLGjRsMk9WO5%2BwpKSkIDAyEhYWF7FqPHj0QEBCA3377jWGy2vG87kTzqAgmhBN2dnbYunUrXrx4Ibv26tUrhIeHw9bWlmEy9fCcv0uXLvj1118Vrp88eVJpgSkkPK%2B7qakpzp8/r3A9IyND8O2ueM7epEkTPHjwQOF6YWEhdHSE3VSK53UnmifsTzMhRCYgIAAzZszAoEGDZMMP8vLyYGxsjB07drANpwae8y9atAhz585FSkqKrM3Y7du3cfHiRWzdupVxOtV4XvfZs2dj1apVuHz5Mnr37g0AyMzMRHx8PD799FPG6VTjOfvYsWPh7%2B%2BPxYsXyz7vmZmZ%2BOqrrzB69GjG6VTjed2J5lF3CEI4UlZWhjNnziA3Nxc6Ojro3LkzBg0ahMaNG8ve8%2BDBA7Rr1w6NGgnvRg/P%2Be/evYsffvgB9%2B7dg46ODjp16oSJEyfC3NycdbRa8bzuZ86cwffffy%2B37t7e3ujXrx/raLXiNbtEIsHmzZtx6NAhPH36FMCbiXHe3t7w9fUV/G4wr%2BtONI%2BKYEK0TN%2B%2BfXHkyBEuijNleM4/cuRI7Nq1C6ampqyj1BnP6%2B7r64uQkBC0a9eOdZQ6E3r2wsJC6OrqokWLFgqv7dq1CxMmTEDLli0ZJPtnhL7uRDOE9ZWfEPKP8f69luf8f/75J16/fs06xlvhed3T09Px6tUr1jHeitCzGxkZKS2AASA8PFzpeGUeCH3diWZQEUwIIYSQOuP5ixMhABXBhBBCCCGkAaIimBBCCCGENDhUBBNCCCGEkAaHimBCtIxIJGId4R/hOT9lJ4QQflARTAgnXr58qdb79PT0uC5oeM7P84NCPK87IYS8jcafffbZZ6xDEEJq17dvX6Snp6OwsBAtWrSAkZGR0vfNnDlTsH07i4uLERsbi/j4ePTu3RsXLlxA48aNYWhoKHuPkPPfvXsXGRkZMDU1xYMHD9CyZUu5wtHR0RGdO3eWG0IhBLyu%2B/Lly/Hq1Su0bdsWBgYGNb6vS5cu6NGjB3R1dTWYTj0lJSXIysqCoaEhXrx4AT09PbnXhZy9NoWFhXB0dFT5vw0r2rzu5N9DwzII4cS9e/dw7tw5nD9/HmlpaWjWrBkGDRoEJycnDBw4EM2bN2cdUaU7d%2B5g2rRpMDU1xZ07d/Drr79ix44dOHbsGCIiIvDee%2B%2BxjlijZ8%2BeYeHChUhLSwMAJCQkYO3atbh//z527dqFDh06ME5YM57XPSwsDCkpKcjMzISlpSWcnJzg5OQEGxsbwU22q%2B7Vq1cIDg7G4cOHAbz5zKxfvx4vXrzAl19%2BKfcFRIji4%2BPxzTffIC8vD3FxcYiOjkbbtm3h6%2BvLOppKvK870Sxh/xYhhMiIxWJMnjwZ27Ztw4ULF7B582Y8f/4cCxcuRP/%2B/VnHq1VISAgmTpyIw4cPy3Zf1q1bh0mTJmHDhg2M06kWEhICAwMDXLhwAfr6%2BgCA0NBQtG/fHiEhIYzTqcbzui9evBixsbFISUmBr68vnj59ipUrV6J///5YtGgR63gqbdy4EVlZWYiLi5N9ZubPn48nT54I/jOzf/9%2BbNiwAWPHjkV5eTkAoHfv3tizZw%2B2bdvGOJ1qPK870TwqggnhRFlZGTIyMrB7927897//ha%2BvL5KTkzFo0CDMnz%2BfdbxaXb9%2BHWPGjFG4PmHCBGRlZTFIpL4zZ85gyZIlcscFjIyM4Ofnh/T0dIbJasfzuleSSCQQiUTQ19eHoaEhSkpKkJmZyTqWSomJiVi1ahW6d%2B8uu9a9e3cEBwfj9OnTDJPVLiYmBiEhIZgyZYpsx3306NHYsGEDDh06xDidajyvO9E8HdYBCCHqsbOzg0QiwaBBg%2BDs7IwFCxbA0tKSm4eZjIyMcO/ePXTs2FHuekZGBtq0acMolfqUjVgtLCyEjo6wf43yvO5%2Bfn7IyMhAfn4%2Bunfvjr59%2B2LmzJno16%2Bf4LOXlpYqPStbUVEBiUTCIJH6CgoKYGFhoXDd3NwcT58%2BZZBIfTyvO9E82gkmhBNz587FgAEDcOnSJRw8eBDff/89fvnlFzx48IB1NLXMmjULq1evxr59%2ByCVSnHhwgVs2bIFQUFBmD59Out4Ko0YMQJr167F3bt3IRKJ8Pz5c1y4cAFr1qyBp6cn63gq1bTugYGBgl/3S5cuIS8vDw4ODhg5ciRGjhwJd3d3wRfAAODq6oqwsDCUlJTIrt2/fx8hISFwdnZmmKx2ffr0wY8//ih3TSqVIjIyEtbW1oxSqYfndSeaRw/GEcIZiUSCmzdv4uLFi8jIyMDp06dhbGyMkydPso5Wq5MnT2LPnj3Izs6GRCKBWCyGj4%2BP4AvJsrIyfPnll9i3bx/Ky8shEonQuHFjfPjhh1i5ciWaNGnCOqJKvK47APz111%2B4ePEi0tPTcfHiRRQUFMDKygr9%2BvXDvHnzWMerUXFxMfz9/ZGUlISKigq0bNkSxcXFGDRoEDZs2IDWrVuzjlijO3fuwNfXF23atEFmZiYGDBiA3NxcvHz5El9//TV69OjBOmKNeF53onlUBBPCmaysLKSmpiI1NRUXL15Eo0aNMHDgQME/5PT69esajw7cvHkTvXr10nCiunv58iXu378PiUQCc3NzNGvWDM%2BfP0fTpk1ZR6tReno6bG1tFda%2BrKwMp0%2Bfhru7O6NkdVNSUoKLFy/ixIkTOHLkCBo1aoSrV6%2ByjlWr%2B/fvIzs7G69fv4ZYLIaFhQWkUqngjzG9evUKR48elfviNGrUKDRr1ox1NLXk5eUhJydHbt0JqY6KYEI4sWDBAly6dAlFRUWws7ODo6MjnJycYGlpyTqaWj755BPs2LFD9sQ2ABQVFeGLL75AbGwsbt68yTCdatu2bVO66/jLL79g/fr1SE5OZpBKPT169EBKSopCX%2BmbN29i4sSJuHbtGqNktUtOTkZqairS0tKQmZmJTp06wdHREY6OjnBwcBD0Dvzhw4cxduxYhetXr15FYGCgrIWXEFW2pKuupKQEX331FVatWsUglXrc3Nzwww8/oFWrVnLXHz58iDFjxuD8%2BfOMkhEhEvYTHYQQGRMTE4SGhgr%2Bj39Nnj9/jmnTpuHrr79GixYtEBsbiy%2B%2B%2BAItW7bEjh07WMdT6cCBA3j27Jnsj39OTg6CgoJw8eJFTJ06lXE6Rfv370dQUBBEIhGkUikcHR2Vvm/gwIEaTlY3y5cvx4ABA/Dxxx/DyckJ7du3Zx1JbcHBwSgqKoKPjw8A4MmTJ9i0aRMOHz6MYcOGsQ1Xi6lTpyI8PBx9%2B/aVXTty5Ag2btwIPT09wRXBx44dk30Rzc/PR1BQkNyX7crrQhtiQ9ijnWBCOFJcXIz4%2BHjk5uZizpw5uHr1KiwsLBSe/BeisrIyLF68GHl5eWjWrBnu3r2LOXPmYNq0aYKf2pSXl4eZM2eiT58%2BaN%2B%2BPaKiojBw4ED4%2B/ujc%2BfOrOMplZ6ejoqKCkybNg1bt26VGxIgEolgYGCAbt26KUzSEpLKYwMlJSXIy8tD165dUVZWJvjBMMCbHd/Zs2fj448/Rvv27REWFgYTExOsXr1a0ANKgDct0sLCwrB582aYmpoiMDAQN27cwMyZMzFr1iyFApO1wsJCbNy4EQAQFxcHDw8PhY2Cpk2bYvTo0YJ/sI9oFhXBhHCC58lflaRSKYKDg/Hdd98hOjoa/fr1Yx1JbYWFhfjPf/6DmzdvIiwsTPC7eZXy8/NhZmYm%2BDOoypSVlSEoKIjb6V%2B5ubmYOXMmCgoK4O/vj0mTJgl%2B0l2lY8eOwd/fH2VlZXB3d8fy5cthZmbGOlattm3bhk8%2B%2BUSQo5yJ8FARTAgnpk6din79%2BmHBggWwtbVFfHw8zM3NsXHjRqSmpiI2NpZ1RAXe3t4KxZdUKsWVK1fQvHlzvPvuu7Lr0dHRmo6nkrLJWM%2BfP0dMTAx69eqFQYMGya4LrUuBn58fVq1ahebNm8PPz0/le9etW6ehVHUXEhKCGzduIDAwEBMmTEB8fDyeP38OPz8/WFhYyHb/hKJ6WzEAePToEbZs2YIhQ4bA1dVVdl3ZABOWCgoKFK6lp6cjICAAS5YswdChQ2XXhVYMV334s7bhNfb29hpKRXhAZ4IJ4cT169eVjv2cMGEC9u3bxyBR7RwcHOp0XUhSU1OVXrexsZF7nccdVl4kJiZi%2B/btSqd/zZgxg2Ey5bZs2aL0etu2bXHjxg3cuHEDwJvPjNCKYFdXV6WfZalUinXr1uHzzz%2BXHU%2B5desWg4Q181QQNVYAABiySURBVPb2RkpKCtq0aQNvb%2B8a3yfE7IQtKoIJ4QSPk7%2BEtkNaFzExMawjvLWqu7sWFhbw8vKCqakpw0Rvh7fpXzz06q5JUlIS6whvreoI7YSEBHTq1IlhGsITKoIJ4UTl5K/Zs2fLJn/FxcXh22%2B/xeLFi1nHU4rn2/I//vgjPD09oaenp/Q2d1VC29WrKjw8nJvzy9VVTv9av3697JqQp3%2Bpe1teJBIJ7jx8hw4dWEf4V0yaNAkRERHo3bs36yiEA1QEE8KJCRMmoF27dtizZw%2BaNGmCDRs2QCwWIzg4mIvJX7zZsmULnJ2doaenV%2BNtbkCYt7arGjFiBHbu3AlfX1%2BYmZkJuhtEdQEBAfD398d7772HiooKjBs3Tjb9a82aNazjKeD5tnyPHj1w9uxZtGnTBpaWliqP%2BQgte1XGxsZ4/Pgx6xiEE/RgHCGEaDFXV1cUFBTUWNQIuaCppGzqmhDl5%2BfL1lnVn1aRSCS4h8vS0tLQt29f6OjoIDU1VWURLORONH5%2BfoiPj4eVlRU6dOig8KVPaHecCFtUBBMiYFXb/SjrVlCVEM/f1pa5KqHlr%2B0p80pCvLVdVVpamsrXhVbQFBQUwNTUFCKRSGnHgqqEVkjWtoNaFQ9fPnjE27ErwhYdhyBEwFJTUzF16lQYGBjU2K0AEG6HAlWZqxJiflW3s6sS4q3tqiqL3NzcXGRnZ6OiogJisRhdu3ZlnEw5V1dX2ZGCyo4FlXs1VXdZhbjuPD9cVlN3CGWE/O9JRS6pC9oJJoQTZWVlXJ3nfFu7du3ChAkT0LJlS9ZRtEJRURH8/PyQlJQEQ0NDSCQSlJaWwt7eHtu3b0eLFi1YR5RTdbjH9OnT4eXlBQcHB6VDJrTlYS4hiIuLU/u9H3zwQT0m%2BedOnDiB3bt3IycnBxKJBGKxGFOmTBH02X3CBhXBhHDCzs4O7u7u8PT0hKOjI3R0tPNGTt%2B%2BfXHkyBGYm5uzjiInOzsb7dq1Q4sWLXDmzBmcPHkSPXv2xPjx41lHU2n58uXIzs7Gxo0b0aVLFwBAVlYWVq5ciW7duiE0NJRxwpr93//9H06dOgUDAwMMGzYMnp6esLOzYx2rwXn16hVu374NsVgsuC9N1X333XdYv349pkyZAltbW1RUVCAjIwMHDhyAv7%2B/4P//SjSLimBCOHH27FkkJiYiKSkJr1%2B/hru7O7y8vNC/f39uRrGqo%2Bo0PKE4ePAggoKCEBUVhebNm%2BOjjz5C//79kZmZifHjx2PhwoWsI9aoX79%2BiIqKgpWVldz1a9euYdasWWofWWGlrKwMZ8%2BexfHjx3Hy5EkYGBjAw8MDnp6eCv9O5N%2BRlZUFf39/rFy5El27dsXHH3%2BMe/fuwcDAADt37kT//v1ZR6yRu7s75s2bp7DrGxcXh/DwcCQkJDBKRoRIe/5yEqLlBg0ahKCgIJw5cwZbtmyBgYEBVq1aBScnJwQFBbGOp9V2796N9evX47333sMPP/yAHj16YPfu3QgLC8OhQ4dYx1NJX19f6ZckkUgkyIET1enp6cHV1RXr1q3DmTNnMGnSJBw8eBAfffQR62haKzAwEObm5ujcuTNiY2NRXFyMs2fPYvbs2XI9m4Xo8ePHsqmOVdna2uJ///sfg0REyKgIJoQzjRo1gp2dHZycnODi4oIXL17g9OnTrGNptYcPH8puw//2229wd3cHALRv3x6lpaUso9XK1dUVgYGByMvLk13Lzc0V7MCJ6iQSCc6dO4fPPvsMrq6u%2BOabbzBy5Eh8%2B%2B23rKNprWvXrmHRokUwMjLCiRMnMHToUBgbG2PEiBHIyclhHU%2BlHj16KB1uExcXJ9iHQQk72nmokBAt9OrVK5w%2BfRoJCQlITk5G06ZNMXz4cERFRaFPnz6s42m1Ll264OjRozAyMkJBQQHc3d1RXl6OyMhIWFpaso6n0rJly/Df//4Xw4YNkz1sWFRUBCcnJ0EOnKhq5cqV%2BO233yCVSuHm5oZ169Zh4MCBaNy4MetoWq1Fixb4%2B%2B%2B/oaOjgytXruA///kPgDdt3YQ6or3SsmXL4OPjg9TUVNnvxStXriAzMxPh4eGM0xGhoSKYEE44ODigadOmGDp0KLZv3w57e3tBthbTRitWrMCiRYvw7NkzTJo0CRYWFggKCsLx48cF/4e1ZcuWiImJQWZmJnJycqCvrw%2BxWCx7SE7IysrKsHbtWgwePLhBdEYRirFjx2LOnDnQ09PDO%2B%2B8g0GDBuHAgQPYsGGDoM%2B/A2%2BOPRw%2BfBjff/89srOzoa%2BvD3t7e4SFhcHU1JR1PCIw9GAcIZw4c%2BaMWrtgvLcYE%2BKDcQBQUVGB4uJiGBoaAgD%2B/vtvGBoaQldXl3Ey1WbMmAEvLy8MHTqU288E0bzjx48jPz8fI0aMgLGxMZKTk1FRUYEhQ4awjqbS0aNH4ebmhqZNm7KOQjhARTAhWkaoLcbUFRoaijlz5qB169ZMc6Snp8PW1hY6Ojq1To%2Bzt7fXUKq6%2B/LLL5GYmIj8/Hw4OjrC09MTbm5uaNasGetohPzrnJ2d8ezZMwwePBgjRoyAs7Mz9PX1WcciAkVFMCFaRqg7qQAQHx%2BPb775Bnl5eYiLi0N0dDTatm0LX19f1tEUWFpayiaXqTr3K8TJZcrcvn0biYmJSExMRF5eHpydneHp6Ynhw4ezjkYEwM3NDbGxsWjdunWt0%2BOEPDEOAC5fviz7rD958gSurq7w9PSEk5OT4O/cEM2iIpgQLSPUInj//v3YsWMHZs%2BejY0bN%2BKnn35CRkYGQkND4e3tjXnz5rGO2CAUFxfjwIEDCA8Px4sXL7go4En9i4uLg5eXF/T09FROjxOJRFxNXrt58yYSEhKwb98%2B6OjoCL4vNtEsejCOEKIRMTExCAkJgYuLC7744gsAwOjRo9GqVSsEBAQIrgguKChQ%2B71mZmb1mOSfKywsRFJSEhITE3HhwgV07doVs2fPhpeXF%2BtoRCAOHz6s9uhkHorg58%2Bf49SpU0hMTMTZs2dhYmICT09P1rGIwFARTAjRiIKCAlhYWChcNzc3x9OnTxkkUq2mW8KVN8%2Bqvibk3VRvb29kZGSgU6dO8PT0hJ%2BfHxedIYhmOTg4yP75yZMnOHjwINzd3WFlZQVdXV3cunULv/zyCyZPnswwZe3i4uKQmJiIc%2BfOwdjYGJ6enti7d6/gWxkSNqgIJoRoRJ8%2BffDjjz9i/vz5smtSqRSRkZGwtrZmmEy5quceT506hZiYGPj5%2BcHKygp6enq4efMmPv/8c8FPLrOxscGqVauoCCAqVb0TM23aNPj7%2B2PSpEly77G3t8fBgwc1Ha1OwsLCMHz4cERHR1P/dFIrOhNMiJYR6pngO3fuwNfXF23atEFmZiYGDBiA3NxcvHz5El9//TV69OjBOmKNXFxc8NVXXyn8Ub1%2B/TrmzJmDs2fPMkqmntevX%2BPx48eyMclSqRRlZWW4desW3SImCmxsbBAXFwexWCx3PTs7G%2BPGjcOVK1cYJaudVCqt8aG%2B8vJyejCOyKGdYEK0zPjx49G8eXPWMRR069YNCQkJiI%2BPR05ODiQSCdzc3DBq1CjBt%2BsqLS3F69evFa6XlJSgvLycQSL1JSUlYfXq1UqPnLRt25aKYKKgZ8%2Be2LVrFz777DNZe7GSkhJs2bIFNjY2jNOp9vjxY0RERCArK0vuS195eTmys7NrbXdIGhbaCSaEIzy1GKvJ8%2BfPkZeXB11dXbzzzjtc9PAMDg7GqVOnsGjRIlhaWkIqleL69evYsmULxowZg8WLF7OOWCMPDw/Y29vDx8cHEydOxK5du/D06VMEBwdj7ty5GDt2LOuIRGCys7Ph6%2BuLZ8%2BeoVOnTpBKpcjNzYWZmRkiIiLQoUMH1hFr5Ovri7y8PLz//vuIjIzE9OnTkZeXh%2BPHj2PlypWYOnUq64hEQGgnmBBOVG8xBgC9e/dGaGgoysrKBNddobri4mKEhobi6NGjkEgkkEql0NPTw4QJE7B06VJBj8X18/NDs2bNsG7dOhQWFgIAjI2NMXnyZMyePZtxOtXu37%2BPiIgIdOzYEb1798ajR4/g7u6ORo0aYcOGDVQEEwUWFhb49ddfce7cOWRnZwMA3n33XQwcOBA6OsIuG9LT0xEZGQlbW1ukpKTAxcUFdnZ22LVrF06fPk1FMJEj7E8zIUSGtxZj1a1Zswb37t1DVFQUevbsCalUiqtXr2Lt2rV48eIFgoODWUeskY6ODpYsWYIlS5bIimAjIyOF9wlxZHXLli3x4sULAIBYLEZmZibc3d3RpUsX/Pnnn4zTEaHS09ODi4sLXFxcWEepE6lUChMTEwBA165d8fvvv8POzg4eHh7Ys2cP43REaBqxDkAIUQ9vLcaqO3XqFNatWwd7e3s0a9YMzZs3h6OjI0JDQ/Hzzz%2Bzjqc2IyMjpQUwAISHh%2BPZs2caTqSas7MzAgMDkZWVBQcHBxw5cgQ3b97EwYMH0a5dO9bxCPlX9ezZE0eOHAEA9OjRAykpKQBAX/iIUlQEE8KJyhZjVQm5xVh1JiYmsl3Uql68eIFWrVoxSPTvE%2BIjFqtWrUKnTp1w48YNuLu7o0%2BfPvjwww%2Bxb98%2BrFixgnU8Qv6xlJQUlJWVAQCWLl2KyMhIfPPNNxg9ejRu3LiBkSNHYt68efDw8GCclAgNPRhHCCd4bDFW9Uns1NRUHDhwAPPmzYOVlRUaN26M27dvY/PmzfDx8YGPjw%2B7oP8SobSn8/Pzw/Lly9G6dWukp6fDxsZGrjVUSUkJ9PX1qV0U0Qq2trb49ddf0b59e7i5uWHv3r3Q1dWFsbExHj58iBMnTqBVq1bw9PSssX0aaZioCCaEI69evZJrMSYWiwXdYkzdAQ0ikUjQU9fUJZQi2NraGvHx8ejcubPslnBNRzgI4Z2zszMGDx6Mvn37ws/PD6tXr66xTSQPI5%2BJ5lARTAhneGwx1lAIpQieMmUK8vLy0KlTJ6Snp8PW1rbGXd/o6GgNpyPk35WUlIStW7eiuLgYBQUFMDExQaNGiqc9RSKR3CRIQqg7BCGc4LnFGPDmwT5VzMzMNJRE%2B23duhXx8fEoLi6WHYcQ6t0CQv4pNzc3uLm5AQBcXV3xww8/oHXr1oxTER5QEUwIJ3huMQa8%2BeMkEolkD49VP5unDcchhKJ169aYNm2a7OdPPvkEBgYGKv87QmzvRkhdnTx5knUEwhE6DkEIJ2xsbLB//3707NlT7vqVK1cwY8YMZGRkMEqmnvz8fLmfJRIJ8vLysHXrVsydOxfOzs6Mkv17QkNDMWfOHC53ofr27YsjR44wP8pBCCGaQjvBhHCC9xZjykatduzYES1btsSyZcsEXwSrM7La39%2BfYcJ/hvZDCCENDRXBhAhY1RZjI0eOxIoVK2psMcYrkUiEhw8fso6hEu8jqwkhhCii4xCECJg2tRjbtm2bwrXS0lIcO3YMPXv2xPbt2xmkUo%2BHhwdWrFgBFxcXuQ4QycnJCAgIQHJyMuuI/5hQOlsQQoim0E4wIQKWmZnJOsK/JjU1Ve5nkUgEXV1djB49GtOnT2eUSj28j6wmhBCiiIpgQjjBe4ux9evXo3379gr9OyUSCTIzM2FoaMgoWe0qR1bPnz9fdo2nkdWEEEIU0XEIQjhhaWnJdYuxmiaX/fHHHxg1ahSuXr3KKFnteBxZXVd0HIIQ0tDQTjAhnKg%2B6ah6izEhOnToEMLDwwG82TkdN26cwk5wUVGR0qMGQtKtWzckJCTIjax2c3MT9Mjquho/fnyNo2YJIUQb0U4wIZy7du0ali1bhoSEBNZRFJSXl%2BPnn39GRUUF/P394e/vjxYtWsheF4lEMDAwQP/%2B/QV9HKISryOr1WnvRgghDQ3tBBPCOSG3GNPV1cWYMWMAAO%2B88w769u0LHR3%2Bfu3wPLKa2rsRQohy/P01IqSBUtVizNHRkUGiuklLS0NaWlqNrwu5GON5ZHVMTAxCQkLg4uKCL774AgAwevRotGrVCgEBAYJed0IIqU9UBBPCCZ5bjAGK%2BSUSCf78808UFRVh2LBhjFKp59SpUwojqx0dHREaGooZM2YIugim9m6EEKIcFcGEcILnFmPAmx1JZUJDQxU6XQgNzyOrqb0bIYQoRw/GEcIJnluMqXL//n2MHTtWbkS0EFTNk5qaigMHDtQ4slrIY6sbQns3Qgh5G1QEEyJgVVuM5efnw9TUVGmLMXNzcxw%2BfJhFxH9s7969CA8Px9mzZ1lHkaNNI6tfvXol195NLBZrVXs3Qgh5G3QcghABGzNmDHR1dWUtxqZPn15jizGhc3V1VTj2UFpaimfPnmHFihWMUtVMm0ZW6%2Bvrw8vLi8v2boQQUl9oJ5gQTqSlpXHbYgwA4uLi5H6ufLCvd%2B/e6NSpE6NU6uF5ZDXP7d0IIaQ%2BURFMCCeUtUiripdWVy9evMAff/yBiooKdOzYkYspZTyPrF60aBHu3buH1atXK7R3s7OzE3RnC0IIqU9UBBPCCW9vb7mfq7cYW79%2BPaNk6ikvL8fGjRuxf/9%2B2Y6kjo4ORo4cicDAQEHvSObn58v9XH1ktbOzM6NktbOxsVFo7wYAV65cwYwZM5CRkcEoGSGEsMXnfVVCGiCeW4wBb1q8JScnY%2BfOnbC1tUVFRQUuX76MkJAQhIWFCfJccKUOHTooXOvYsSNatmyJZcuWCboI5rm9GyGE1CfaCSaEc0JtMVZd//798dVXX8HBwUHu%2BoULF7B06VLBdYdQx/Xr1%2BHt7Y0rV66wjiJHW9q7EUJIfaKdYEI4l5yczMWT/lKpFG3atFG4bmRkhNLSUgaJ1MfbyOrqR2cAIDAwUOHa%2BvXrqQgmhDRYVAQTwgneWoxV179/f2zatAmbNm2SPQxXVFSEL7/8UmF3WGh4G1mtTe3dCCGkvtBxCEI4wXOLMQB4%2BPAhpk6dir/%2B%2BgtisRgAcO/ePZibm2Pnzp1Kz90KRUFBgcqR1b169WKUrHY8t3cjhJD6REUwIZzhscUYAKxYsQLDhg2DVCpFTk4O9PX1IRaL4ejoqFBcCg3PI6t5bu9GCCH1iY5DEMIJnluMAUDz5s3x6aefory8HO%2B//z48PT3h4OAg2M4WVUdWS6VSjBs3TunIagsLCxbx1JaUlCT3c/X2boQQ0lDRTjAhnAgJCUFycjICAgIUWoy5u7tzcS5YKpUiPT0dx44dQ2JiIgDAw8MDXl5esLGxYZxOXnl5OX7%2B%2BWfZyGp/f/8aR1YbGhoyTPp2rl27hmXLliEhIYF1FEIIYYKKYEI4oW0txkpKSrB7925ERUWhrKwMZmZm%2BOijj%2BDj4yO4bhe8j6xWRqjt3QghRFO05zc6IVqO5xZjlUpLS/Hbb7/h2LFjOHv2LExMTDB9%2BnR4enri0aNH2LRpE9LS0rBnzx7WUeWkpaUhLS2txteFPLKat/ZuhBCiKVQEE8IJnluMAcCcOXNw7tw5tGzZEh4eHoiOjoa1tbXs9W7duqGoqAirVq1imFK56i3Sqo%2BsFjLe2rsRQoim0HEIQjjBc4sxAFizZg28vLxUPgz36NEjLh42q1Q5strPz491lBrx3N6NEELqExXBhHCC5xZj2oqHkdU8t3cjhJD6RMchCOEEby3GGgKhjqzWlvZuhBBSn2gnmBCO8NRiTJvUNrLax8eHTbAaaHt7N0II%2BTdQEUwIp3hqMcY7nkdWa2N7N0II%2BTdQEUwIR5S1GPPw8JBrMWZkZCS4FmPagseR1cpapFUl5PZuhBBSn2hrgBBO8NxijHc8j6zmub0bIYTUJyqCCeGEsbExIiIiVD4M169fPxw6dEjDybTf%2BvXrkZycjJ07dyqMrA4LCxP0yOqYmBil1yvbuxFCSENFxyEIIaQW2jayGuCjvRshhNQnai5KCCG10IaR1dUJtb0bIYRoCh2HIISQWvA8srq29m6EENJQ0XEIQgipBc8jq3lu70YIIfWJimBCCKmFNoys5rG9GyGE1Cc6DkEIIbXgeWQ1z%2B3dCCGkPtFOMCGEqIHXkdUhISFITk5GQECAQns3d3d3OhdMCGmwqAgmhJA64mlktTa2dyOEkH8DHYcghBA1KBtZPX36dLmR1WlpaYIbWa2N7d0IIeTfQEUwIYTUgueR1Ty3dyOEkPpExyEIIaQWa9asgZeXl8qH4R49eoSioiJYWFhoOJ1qPLd3I4SQ%2BkRFMCGEaDFtaO9GCCH1gY5DEEKIFuO5vRshhNQn2gkmhBAtx2t7N0IIqU9UBBNCSAPCU3s3QgipT1QEE0KIllPW3s3Dw0OuvZuRkZHg2rsRQkh9ojPBhBCixXhu70YIIfWJimBCCNFixsbGiIiIUPkwXL9%2B/XDo0CENJyOEELboOAQhhBBCCGlwqEkkIYQQQghpcKgIJoQQQgghDQ4VwYQQQgghpMGhIpgQQgghhDQ4VAQTQgghhJAGh4pgQgghhBDS4FARTAghhBBCGhwqggkhhBBCSIPz/%2BVr4aNHVM0bAAAAAElFTkSuQmCC\" class=\"center-img\">\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Sample</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
       "        <table border=\"1\" class=\"dataframe sample\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>created</th>\n",
       "      <th>youtube_view_count</th>\n",
       "      <th>youtube_like_count</th>\n",
       "      <th>youtube_dislike_count</th>\n",
       "      <th>youtube_favorite_count</th>\n",
       "      <th>view_count</th>\n",
       "      <th>like_count</th>\n",
       "      <th>dislike_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16243</td>\n",
       "      <td>2017-12-28 00:02:02</td>\n",
       "      <td>371</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>552</td>\n",
       "      <td>2017-12-22 01:49:42</td>\n",
       "      <td>588</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>551</td>\n",
       "      <td>2017-12-22 01:49:22</td>\n",
       "      <td>619</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>550</td>\n",
       "      <td>2017-12-22 01:49:01</td>\n",
       "      <td>329</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>549</td>\n",
       "      <td>2017-12-22 01:48:33</td>\n",
       "      <td>765</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "    </div>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "<pandas_profiling.ProfileReport at 0x10c903f60>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.ProfileReport(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Youtube Statistics Analysis <a class=\"anchor\" id=\"youtube-statistics-analysis\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Youtube Views <a class=\"anchor\" id=\"youtube-views\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data_set = pd.DataFrame({\n",
    "    'created': data_set.created,\n",
    "    'youtube_views': data_set.youtube_view_count,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Descriptive analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Count: 4954'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = data_set.youtube_view_count.count()\n",
    "\"Count: {:d}\".format(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Minimum, Index Minimum, Maximum, Index Maximum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Minimum: 0 Index Minimum: 2046 - Maximum 2929460 Index Maximum: 3758'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min = data_set.youtube_view_count.min()\n",
    "max = data_set.youtube_view_count.max()\n",
    "index_min = data_set.youtube_view_count.idxmin()\n",
    "index_max = data_set.youtube_view_count.idxmax()\n",
    "\"Minimum: {:d} Index Minimum: {:d} - Maximum {:d} Index Maximum: {:d}\".format(min, index_min, max, index_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Quantile 50%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Quantile 50%: 684.500000'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantile = data_set.youtube_view_count.quantile()\n",
    "\"Quantile 50%: {:f}\".format(quantile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sum: 19942581'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum = data_set.youtube_view_count.sum()\n",
    "\"Sum: {:d}\".format(sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mean: 4025.551272'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = data_set.youtube_view_count.mean()\n",
    "\"Mean: {:f}\".format(mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Arithmetic median (50% quantile) of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Arithmetic median (50% quantile) of values 684.500000'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = data_set.youtube_view_count.median()\n",
    "\"Arithmetic median (50% quantile) of values {:f}\".format(median)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Mean absolute deviation from mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mean absolute deviation from mean value 5336.561720'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mad = data_set.youtube_view_count.mad()\n",
    "\"Mean absolute deviation from mean value {:f}\".format(mad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Product of all values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Product of all values 0.000000'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod = data_set.youtube_view_count.prod()\n",
    "\"Product of all values {:f}\".format(prod)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sample variance of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sample variance of values 1908813912.226827'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = data_set.youtube_view_count.var()\n",
    "\"Sample variance of values {:f}\".format(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sample standard deviation of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sample standard deviation of values 43689.974963'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = data_set.youtube_view_count.std()\n",
    "\"Sample standard deviation of values {:f}\".format(std) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sample skewness (third moment) of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sample skewness (third moment) of values 61.064337'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew = data_set.youtube_view_count.skew()\n",
    "\"Sample skewness (third moment) of values {:f}\".format(skew)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sample kurtosis (fourth moment) of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sample kurtosis (fourth moment) of values 4063.294041'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kurt = data_set.youtube_view_count.kurt()\n",
    "\"Sample kurtosis (fourth moment) of values {:f}\".format(kurt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cumsum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     371\n",
       "1     959\n",
       "2    1578\n",
       "3    1907\n",
       "4    2672\n",
       "Name: youtube_view_count, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumsum = data_set.youtube_view_count.cumsum()\n",
    "cumsum.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cummin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    371\n",
       "1    371\n",
       "2    371\n",
       "3    329\n",
       "4    329\n",
       "Name: youtube_view_count, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cummin = data_set.youtube_view_count.cummin()\n",
    "cummin.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cummax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    371\n",
       "1    371\n",
       "2    371\n",
       "3    329\n",
       "4    329\n",
       "Name: youtube_view_count, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cummax = data_set.youtube_view_count.cummin()\n",
    "cummax.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cumprod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               371\n",
       "1            218148\n",
       "2         135033612\n",
       "3       44426058348\n",
       "4    33985934636220\n",
       "Name: youtube_view_count, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumprod = data_set.youtube_view_count.cumprod()\n",
    "cumprod.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "1    217.0\n",
       "2     31.0\n",
       "3   -290.0\n",
       "4    436.0\n",
       "Name: youtube_view_count, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = data_set.youtube_view_count.diff()\n",
    "diff.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Percent change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1    0.584906\n",
       "2    0.052721\n",
       "3   -0.468498\n",
       "4    1.325228\n",
       "Name: youtube_view_count, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pct_change = data_set.youtube_view_count.pct_change()\n",
    "pct_change.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LX9CxY98rNXWXMjO/1bXXdqnJ7qgzAjIAAEAd3XTTUNntJbrppqGSpMDAQP31ry9Ikh5+eLxmzpyhgQN/qyefnCGp/ES5uLg/acWKZZo48T65XNKgQYPd/cXE9NYVV3TQ+PF3KzPzW919933q3LmL4uIe1qxZCZow4cFK2zB27L3661/n6LHHHtV11/XSI49MkSQ1b95CixYtVlbWUd1//7167rl5GjlyjP7wh/u9/v6uu66nQkNDdeONg2U2Vz0B4aabhurhhx/V66+/qj/8YaxSUnbquedeVIcOV0oqPyreu3cfhYW10uWXXy5JslrLg3Lv3uV3N77kkkv017++oMzMbzV+/N36y1+m6/bb79B//dcIr7e1PnCjkDrgRiG/LNTcty5G/e1lTvV/eYsk6Vetm+vdCU3zgvoNgde/77EPfKuu9d+5c5uee26ekpP/XeX8YpxfY7tRCHOQAfgnDg0AqAd5eXlKT0/VP/+5TMOH30k4/oUgIAMAANTSmTNFWrBgtnr0uE533/17X29Ojb388vPasOG9atv/8If7NW7cxIu4RY0DARmAf+IgD4B60LHjr/Txx5/7ejNqbdy4iRo5cky17Rf7+sONBQEZAADAT4WFhdXoahb+gqtYAPBPzEEGAFSDgAzAb3DuDADAGwRkAH6Di1oCALxBQAbgnziaDACoBgEZAAAAMCAgA/BPTLcAAFSDgAzAb5CJAQDeICAD8E/MQQYAVIOADAAAABgQkAH4J+ZbAACqQUAGAAAADAjIAPyGizuFAAC8QEAG4J84SQ8AUA0CMgD/xMFkAEA1CMgAAACAAQEZAAAAMCAgAwAAAAYEZAAAAMCAgAwAAAAYEJABAAAAAwIyAL/Bld0AAN4gIAPwT9woBABQDQIyAP/E4WQAQDUIyAAAAIABARkAAAAwICAD8BsuplUAALxAQAYAAAAMCMgAAACAAQEZAAAAMCAgAwAAAAYEZAB+w8XFjwEAXiAgAwAAAAYEZAAAAMCgxgH55MmTio+PV58+fTRw4EAtWLBAJSUlkqS5c+eqS5cuHv9WrlzpXnfDhg26+eabZbVaFRcXp1OnTrnbXC6XFi1apH79+qlPnz5KTEyU0+l0t+fn52vKlCmKjo7W4MGDtX79eo/t2r9/v0aPHi2r1aqRI0dq7969Hu3nGxsAAACoUKOA7HK5FB8fL5vNplWrVunFF1/Up59+qpdeekmSdPDgQT3xxBPasmWL+9/IkSMlSenp6UpISNDkyZO1evVqFRYWavr06e6+ly9frg0bNigpKUmLFy/W+++/r+XLl7vbp0+frqKiIq1evVqPPPKInn76aaWnp0uSzp07p4cfflixsbFat26doqOjNWnSJJ07d86rsQH4B24UAgDwRo0C8qFDh5SamqoFCxbo2muvVWxsrOLj47VhwwZJ5QG5e/fuCg8Pd/+zWCySpJUrV2rYsGEaMWKEunbtqsTERG3evFlZWVmSpDfffFPx8fGKjY1Vv379NHXqVK1atUqSdPToUX366aeaO3euOnfurNGjR+uOO+7QW2+9JUn64IMPFBQUpGnTpqlTp05KSEhQixYttHHjRq/GBgAAACrUKCCHh4fr9ddfV5s2bTyWnzlzRmfOnNHJkyfVsWPHKtdNS0tTbGys++u2bduqXbt2SktL08mTJ3XixAn17t3b3R4TE6Njx44pJydHaWlpatu2rdq3b+/Rvnv3bnffMTExMplMkiSTyaTrr79eqampFxwbAAAAMDLX5MkhISEaOHCg+2un06mVK1eqX79+OnjwoEwmk1599VV9/vnnCg0N1f3336/f/e53kqScnBxFRER49Ne6dWtlZ2crNzdXkjzaK0J4RXtV6548eVKSlJubq2uuuaZS+4EDBy44trdycnLc21nBbG5eqd/6EhgY4PE/Gh41962LUX+zw9C3STKb2dcVeP37HvvAt6i/bzW2+tcoIP/cwoULtX//fq1Zs0b79u2TyWTS1Vdfrd///vfauXOnnnnmGbVs2VJDhgxRcXGxmjVr5rF+s2bNZLfbVVxc7P7a2CZJdrtdNput2nUlXbD9fGN7a/Xq1UpKSvJYFhcXp/j4eK/7qI2QEEuD9o/KqLlvNWT9zcWl7seBgQEKC2vRYGM1Vbz+fY994FvU37caS/1rHZAXLlyoFStW6MUXX1Tnzp117bXXatCgQQoNDZUkde3aVYcPH9bbb7+tIUOGKCgoqFIgtdvtslgsHmE4KCjI/ViSLBZLtesGBwdLUq3bK+ZHe2Ps2LEaPHiwxzKzubny88963UdNBAYGKCTEosJCmxwO54VXQJ1Rc9+6GPUvKilzP3Y4nA32/m2KeP37HvvAt6i/bzVk/WtzMKRWAXnOnDl6++23tXDhQt1yyy2Syuf9VoTjCldffbW2bdsmSYqMjFReXp5He15ensLDwxUZGSmpfKpExTzjiukMFe3VrXu+viumP1xofW9ERERUmk6Rm1uksrKGfRM5HM4GHwOeqLlvNWT9HcZ+XWI/V4HXv++xD3yL+vtWY6l/jSd6JCUl6Z133tELL7yg22+/3b385Zdf1oQJEzyem5GRoauvvlqSZLValZKS4m47ceKETpw4IavVqsjISLVr186jPSUlRe3atVNERISioqJ07NgxjznDKSkpioqKcve9e/duuX68hpPL5dKuXbtktVovODYAAABgVKOAfPDgQb3yyit66KGHFBMTo9zcXPe/QYMGaefOnVq6dKmOHj2qt956S++9954mTpwoSbrnnnu0fv16JScnKyMjQ9OmTdONN96oDh06uNsXLVqk7du3a/v27Xr++ec1btw4SVKHDh00YMAAPfnkk8rIyFBycrI2bNig++67T5J06623qrCwUPPmzVNmZqbmzZsnm82mYcOGeTU2AAAAUMHkcnl/6fwlS5bo+eefr7Ltm2++0aZNm7R48WIdPnxYV1xxhR577DENHTrU/Zx169Zp8eLFOn36tPr37685c+YoLCxMkuRwOJSYmKh169YpMDBQo0aN0hNPPOG+dNsPP/yghIQEbd26VeHh4Xrsscc0fPhwd9/p6emaOXOmDh48qC5duujZZ59V9+7dvRq7tnJzi+q0/vmYzeUnEOXnn20UHzX4A2ruWxej/mdKyjQoaaskqWMri5Lv732BNfwHr3/fYx/4FvX3rYasf3j4pTVep0YBGZ4IyL8s1Ny3Lkb9i4rLNPjvBOSq8Pr3PfaBb1F/32psAblxXGwOAC4yk0y+3gQAQCNFQAYAAAAMCMgA/JJLzC4DAFSNgAzAbxCKAQDeICAD8EvMQQYAVIeADAAAABgQkAH4JaZbAACqQ0AGAAAADAjIAPwGt0UCAHiDgAzAL3GSHgCgOgRkAAAAwICADMAvcZIeAKA6BGQAAADAgIAMwG9wzBgA4A0CMgC/xEl6AIDqEJAB+CXmIAMAqkNABgAAAAwIyAD8BweNAQBeICAD8EvMQQYAVIeADMAvMQcZAFAdAjIAAABgQEAGAAAADAjIAPwG0yoAAN4gIAMAAAAGBGQAAADAgIAMAAAAGBCQAQAAAAMCMgC/wSl6AABvEJABAAAAAwIyAAAAYEBABgAAAAwIyAD8hotJyAAALxCQAQAAAAMCMgAAAGBAQAYAAAAMCMgAAACAAQEZgN/gHD0AgDcIyAAAAIABARkAAAAwICADAAAABgRkAP7DcKcQbhoCAKgOARkAAAAwICAD8Esmk6+3AADQWBGQAQAAAAMCMgC/xBxkAEB1CMgA/AaZGADgjRoF5JMnTyo+Pl59+vTRwIEDtWDBApWUlEiSsrKyNGHCBEVFRem2227Tli1bPNbdunWrhg8fLqvVqnHjxikrK8uj/Y033tDAgQMVHR2tGTNmyGazudtKSko0Y8YMxcbGasCAAVq2bJnHunUdG4D/YQ4yAKA6Xgdkl8ul+Ph42Ww2rVq1Si+++KI+/fRTvfTSS3K5XIqLi1ObNm20du1a3XnnnZo8ebKOHz8uSTp+/Lji4uJ01113ac2aNWrVqpUeffRRuX78jPPDDz9UUlKSZs+erRUrVigtLU0LFy50j52YmKi9e/dqxYoVmjlzppKSkrRx40b3dtVlbAAAAMDI64B86NAhpaamasGCBbr22msVGxur+Ph4bdiwQdu2bVNWVpZmz56tTp06adKkSYqKitLatWslScnJybruuus0ceJEXXvttVqwYIGOHTumHTt2SJLefPNNjR8/XoMGDVKvXr307LPPau3atbLZbDp37pySk5OVkJCgHj16aMiQIXrwwQe1atUqSarz2AAAAICR1wE5PDxcr7/+utq0aeOx/MyZM0pLS1P37t3VvHlz9/KYmBilpqZKktLS0hQbG+tus1gs6tGjh1JTU+VwOLRnzx6P9qioKJWWliojI0MZGRkqKytTdHS0R99paWlyOp11GhuA/+JDJABAdczePjEkJEQDBw50f+10OrVy5Ur169dPubm5ioiI8Hh+69atlZ2dLUnnbS8sLFRJSYlHu9lsVmhoqLKzsxUQEKCwsDA1a9bM3d6mTRuVlJSooKCgTmPXRE5OjnJzcz2Wmc3NK/VdXwIDAzz+R8Oj5r51Mepv7Ntkksxm9nUFXv++xz7wLervW42t/l4H5J9buHCh9u/frzVr1uiNN97wCLCS1KxZM9ntdkmSzWartr24uNj9dVXtLperyjZJstvt5+37QmPXxOrVq5WUlOSxLC4uTvHx8TXqp6ZCQiwN2j8qo+a+1ZD1LwkIdD8ODAxQWFiLBhurqeL173vsA9+i/r7VWOpfq4C8cOFCrVixQi+++KI6d+6soKAgFRQUeDzHbrcrODhYkhQUFFQpkNrtdoWEhCgoKMj99c/bLRaLHA5HlW2SFBwcXKexa2Ls2LEaPHiwxzKzubny88/WqB9vBQYGKCTEosJCmxwOZ4OMAU/U3LcuRv1PF5W4HzsdrgZ7/zZFvP59j33gW9Tftxqy/rU5GFLjgDxnzhy9/fbbWrhwoW655RZJUmRkpDIzMz2el5eX555+EBkZqby8vErt3bp1U2hoqIKCgpSXl6dOnTpJksrKylRQUKDw8HC5XC7l5+errKxMZnP55ubm5io4OFghISF1GrsmIiIiKk2nyM0tUllZw76JHA5ng48BT9Tctxqy/sZ+nS4X+7kKvP59j33gW9TftxpL/Ws00SMpKUnvvPOOXnjhBd1+++3u5VarVfv27XNPl5CklJQUWa1Wd3tKSoq7zWazaf/+/bJarQoICFDPnj092lNTU2U2m9W1a1d169ZNZrPZ46S6lJQU9ezZUwEBAXUaG4B/4bw8AIA3vA7IBw8e1CuvvKKHHnpIMTExys3Ndf/r06eP2rZtq+nTp+vAgQNasmSJ0tPTNWrUKEnSyJEjtWvXLi1ZskQHDhzQ9OnT1b59e/Xt21eSdO+992rp0qXatGmT0tPTNWvWLI0ZM0YWi0UWi0UjRozQrFmzlJ6erk2bNmnZsmUaN26cJNV5bAAAAMDI5PLyjhlLlizR888/X2XbN998oyNHjighIUFpaWm66qqrNGPGDP361792P2fz5s2aP3++srOzFR0drTlz5qhDhw4e/b/xxhuy2+0aOnSoZs6c6Z6fbLPZNGvWLH300Udq2bKlHnjgAU2YMMG9bl3Hrq3c3KI691Eds7n8BKL8/LON4qMGf0DNfeti1P9kUYmGL9kuSerYyqLk+3s3yDhNEa9/32Mf+Bb1962GrH94+KU1XsfrgIzKCMi/LNTcty52QL4qzKI1EwnIFXj9+x77wLeov281toDcOC42BwAAADQSBGQAfoMPzAAA3iAgA/BLJpOvtwAA0FgRkAH4JQ4mAwCqQ0AGAAAADAjIAAAAgAEBGQAAADAgIAPwS5ykBwCoDgEZgF/iJD0AQHUIyAAAAIABARmA3+CgMQDAGwRkAH6JOcgAgOoQkAH4JeYgAwCqQ0AGAAAADAjIAAAAgAEBGYDfYFoFAMAbBGQAAADAgIAMAAAAGBCQAQAAAAMCMgAAAGBAQAbgN1zcSw8A4AUCMgAAAGBAQAYAAAAMCMgAAACAAQEZgN/gRiEAAG8QkAEAAAADAjIAAABgQEAGAAAADAjIAAAAgAEBGQAAADAgIAOqxAX3AAAgAElEQVQAAAAGBGQAAADAgIAMAAAAGBCQAfgl7hkCAKgOARmA3+BOegAAbxCQAfglk683AADQaBGQAQAAAAMCMgC/xGwLAEB1CMgA/AahGADgDQIyAL/EHGQAQHUIyAAAAIABARkAAAAwICAD8EvMRwYAVIeADMBvuLhTCADACwRkAH6Jk/QAANUhIAMAAAAGtQ7Idrtdw4cP1/bt293L5s6dqy5dunj8W7lypbt9w4YNuvnmm2W1WhUXF6dTp06521wulxYtWqR+/fqpT58+SkxMlNPpdLfn5+drypQpio6O1uDBg7V+/XqP7dm/f79Gjx4tq9WqkSNHau/evR7t5xsbgP9hsgUAoDq1CsglJSV6/PHHdeDAAY/lBw8e1BNPPKEtW7a4/40cOVKSlJ6eroSEBE2ePFmrV69WYWGhpk+f7l53+fLl2rBhg5KSkrR48WK9//77Wr58ubt9+vTpKioq0urVq/XII4/o6aefVnp6uiTp3LlzevjhhxUbG6t169YpOjpakyZN0rlz57waGwAAAKhQ44CcmZmpMWPG6OjRo5XaDh48qO7duys8PNz9z2KxSJJWrlypYcOGacSIEeratasSExO1efNmZWVlSZLefPNNxcfHKzY2Vv369dPUqVO1atUqSdLRo0f16aefau7cuercubNGjx6tO+64Q2+99ZYk6YMPPlBQUJCmTZumTp06KSEhQS1atNDGjRu9GhuAf+CoMQDAGzUOyDt27FDfvn21evVqj+VnzpzRyZMn1bFjxyrXS0tLU2xsrPvrtm3bql27dkpLS9PJkyd14sQJ9e7d290eExOjY8eOKScnR2lpaWrbtq3at2/v0b5792533zExMTKZyk+7MZlMuv7665WamnrBsQH4J07SAwBUx1zTFe69994qlx88eFAmk0mvvvqqPv/8c4WGhur+++/X7373O0lSTk6OIiIiPNZp3bq1srOzlZubK0ke7W3atJEkd3tV6548eVKSlJubq2uuuaZSe8UUkPON7a2cnBz3dlYwm5tX6re+BAYGePyPhkfNfeti1N9s7Nskmc3s6wq8/n2PfeBb1N+3Glv9axyQq3Po0CGZTCZdffXV+v3vf6+dO3fqmWeeUcuWLTVkyBAVFxerWbNmHus0a9ZMdrtdxcXF7q+NbVL5yYA2m63adSVdsP18Y3tr9erVSkpK8lgWFxen+Ph4r/uojZAQS4P2j8qouW81ZP1Plf00ySIwIEBhYS0abKymite/77EPfIv6+1ZjqX+9BeQRI0Zo0KBBCg0NlSR17dpVhw8f1ttvv60hQ4YoKCioUiC12+2yWCweYTgoKMj9WJIsFku16wYHB0tSrdsr5kd7Y+zYsRo8eLDHMrO5ufLzz3rdR00EBgYoJMSiwkKbHA7nhVdAnVFz37oY9T99+pz7scPpbLD3b1PE69/32Ae+Rf19qyHrX5uDIfUWkE0mkzscV7j66qu1bds2SVJkZKTy8vI82vPy8hQeHq7IyEhJ5VMlKuYZV0xnqGivbt3z9V0x/eFC63sjIiKi0nSK3NwilZU17JvI4XA2+BjwRM19qyHr7zAcQZZL7Ocq8Pr3PfaBb1F/32os9a+3iR4vv/yyJkyY4LEsIyNDV199tSTJarUqJSXF3XbixAmdOHFCVqtVkZGRateunUd7SkqK2rVrp4iICEVFRenYsWMec4ZTUlIUFRXl7nv37t3u28i6XC7t2rVLVqv1gmMD8E9c0QIAUJ16C8iDBg3Szp07tXTpUh09elRvvfWW3nvvPU2cOFGSdM8992j9+vVKTk5WRkaGpk2bphtvvFEdOnRwty9atEjbt2/X9u3b9fzzz2vcuHGSpA4dOmjAgAF68sknlZGRoeTkZG3YsEH33XefJOnWW29VYWGh5s2bp8zMTM2bN082m03Dhg3zamwAAACgQr1NsejVq5defvllLV68WC+//LKuuOIKPf/884qOjpYkRUdHa/bs2Vq8eLFOnz6t/v37a86cOe71H3jgAf3www+aPHmyAgMDNWrUKI8j0omJiUpISNCYMWMUHh6u+fPnq1evXpKkli1b6rXXXtPMmTP17rvvqkuXLlqyZImaN2/u1dgAAABABZOrYl4Caiw3t6jB+jaby8+wz88/2yjm4vgDau5bF6P+3/1wTmPe+EqSdGWYRWsn9r7AGv6D17/vsQ98i/r7VkPWPzz80hqv0zguNgcAAAA0EgRkAAAAwICADAAAABgQkAEAAAADAjIAv+Hi6scAAC8QkAEAAAADAjIAAABgQEAGAAAADAjIAPwGt0UCAHiDgAwAAAAYEJABAAAAAwIyAAAAYEBABgAAAAwIyAD8BufoAQC8QUAGAAAADAjIAAAAgAEBGQAAADAgIAMAAAAGBGQA/oOz9AAAXiAgAwAAAAYEZAAAAMCAgAwAAAAYEJAB+A0Xk5ABAF4gIAMAAAAGBGQAAADAgIAMAAAAGBCQAQAAAAMCMgC/4eIcPQCAFwjIAAAAgAEBGQAAADAgIAMAAAAGBGQAAADAgIAMwG9wjh4AwBsEZAAAAMCAgAwAAAAYEJABAAAAAwIyAP/BJGQAgBcIyAAAAIABARkAAAAwICADAAAABgRkAAAAwICADMBvuDhLDwDgBQIyAAAAYEBABgAAAAwIyAAAAIABARkAAAAwqHVAttvtGj58uLZv3+5elpWVpQkTJigqKkq33XabtmzZ4rHO1q1bNXz4cFmtVo0bN05ZWVke7W+88YYGDhyo6OhozZgxQzabzd1WUlKiGTNmKDY2VgMGDNCyZcs81q3r2AB++ThFDwDgjVoF5JKSEj3++OM6cOCAe5nL5VJcXJzatGmjtWvX6s4779TkyZN1/PhxSdLx48cVFxenu+66S2vWrFGrVq306KOPyuUq/5X14YcfKikpSbNnz9aKFSuUlpamhQsXuvtPTEzU3r17tWLFCs2cOVNJSUnauHFjvYwNAAAAVKhxQM7MzNSYMWN09OhRj+Xbtm1TVlaWZs+erU6dOmnSpEmKiorS2rVrJUnJycm67rrrNHHiRF177bVasGCBjh07ph07dkiS3nzzTY0fP16DBg1Sr1699Oyzz2rt2rWy2Ww6d+6ckpOTlZCQoB49emjIkCF68MEHtWrVqnoZGwAAAKhQ44C8Y8cO9e3bV6tXr/ZYnpaWpu7du6t58+buZTExMUpNTXW3x8bGutssFot69Oih1NRUORwO7dmzx6M9KipKpaWlysjIUEZGhsrKyhQdHe3Rd1pampxOZ53GBgAAAIzMNV3h3nvvrXJ5bm6uIiIiPJa1bt1a2dnZF2wvLCxUSUmJR7vZbFZoaKiys7MVEBCgsLAwNWvWzN3epk0blZSUqKCgoE5jeysnJ0e5ubkey8zm5pX6rS+BgQEe/6PhUXPfuhj1DwgweXxtNrOvK/D69z32gW9Rf99qbPWvcUCujs1m8wiwktSsWTPZ7fYLthcXF7u/rqrd5XJV2SaVnyxYl7G9tXr1aiUlJXksi4uLU3x8vNd91EZIiKVB+0dl1Ny3GrL+IWdL3Y8DAwMUFtaiwcZqqnj9+x77wLeov281lvrXW0AOCgpSQUGBxzK73a7g4GB3+88Dqd1uV0hIiIKCgtxf/7zdYrHI4XBU2SZJwcHBdRrbW2PHjtXgwYM9lpnNzZWff9brPmoiMDBAISEWFRba5HA4G2QMeKLmvnUx6l9Y+NOVcRwOZ4O9f5siXv++xz7wLervWw1Z/9ocDKm3gBwZGanMzEyPZXl5ee4pCJGRkcrLy6vU3q1bN4WGhiooKEh5eXnq1KmTJKmsrEwFBQUKDw+Xy+VSfn6+ysrKZDaXb3Jubq6Cg4MVEhJSp7G9FRERUWk6RW5ukcrKGvZN5HA4G3wMeKLmvtWQ9Xc4PK9cw36ujNe/77EPfIv6+1ZjqX+9TfSwWq3at2+fe7qEJKWkpMhqtbrbU1JS3G02m0379++X1WpVQECAevbs6dGempoqs9msrl27qlu3bjKbzR4n1aWkpKhnz54KCAio09gAAACAUb0F5D59+qht27aaPn26Dhw4oCVLlig9PV2jRo2SJI0cOVK7du3SkiVLdODAAU2fPl3t27dX3759JZWf/Ld06VJt2rRJ6enpmjVrlsaMGSOLxSKLxaIRI0Zo1qxZSk9P16ZNm7Rs2TKNGzeuXsYG4B+48jkAwBv1FpADAwP1yiuvKDc3V3fddZf+/e9/6+9//7vatWsnSWrfvr3+9re/ae3atRo1apQKCgr097//XSZT+Vnlt99+uyZNmqS//OUvmjhxonr16qUnn3zS3f/06dPVo0cPjR8/Xs8++6ymTJmioUOH1svYAAAAQAWTi9vJ1VpublGD9W02l59hn59/tlHMxfEH1Ny3Lkb992UXacKq3ZKkK8MsWjuxd4OM0xTx+vc99oFvUX/fasj6h4dfWuN1GsfF5gAAAIBGgoAMwH/wgRkAwAsEZAAAAMCAgAwAAAAYEJABAAAAAwIyAAAAYEBABuA3OEUPAOANAjIAAABgQEAGAAAADAjIAAAAgAEBGYBfcnHTEABANQjIAPwGmRgA4A0CMgC/ZDKZfL0JAIBGioAMAAAAGBCQAQAAAAMCMgC/YZyCzEl6AIDqEJABAAAAAwIyAL/ESXoAgOoQkAEAAAADAjIAv8QcZABAdQjIAPwGoRgA4A0CMgC/xBxkAEB1CMgAAACAAQEZgF9iugUAoDoEZAAAAMCAgAwAAAAYEJAB+CVO0gMAVIeADMAvMQcZAFAdAjIAAABgQEAG4Dc4aAwA8AYBGQAAADAgIAMAAAAGBGQAAADAgIAMAAAAGBCQAfgNztEDAHiDgAzAL3GjEABAdQjIAPwSNwoBAFSHgAwAAAAYEJABAAAAAwIyAL/h4jQ9AIAXCMgAAACAAQEZAAAAMCAgAwAAAAYEZAB+gyu7AQC8QUAGAAAADAjIAAAAgEG9BuSPP/5YXbp08fgXHx8vSdq/f79Gjx4tq9WqkSNHau/evR7rbtiwQTfffLOsVqvi4uJ06tQpd5vL5dKiRYvUr18/9enTR4mJiXI6ne72/Px8TZkyRdHR0Ro8eLDWr1/v0feFxgYAAAAq1GtAzszM1KBBg7Rlyxb3v7lz5+rcuXN6+OGHFRsbq3Xr1ik6OlqTJk3SuXPnJEnp6elKSEjQ5MmTtXr1ahUWFmr69OnufpcvX64NGzYoKSlJixcv1vvvv6/ly5e726dPn66ioiKtXr1ajzzyiJ5++mmlp6dL0gXHBgAAAIzqNSAfPHhQnTt3Vnh4uPtfSEiIPvjgAwUFBWnatGnq1KmTEhIS1KJFC23cuFGStHLlSg0bNkwjRoxQ165dlZiYqM2bNysrK0uS9Oabbyo+Pl6xsbHq16+fpk6dqlWrVkmSjh49qk8//VRz585V586dNXr0aN1xxx166623JOmCYwMAAABG9R6QO3bsWGl5WlqaYmJiZDKZJEkmk0nXX3+9UlNT3e2xsbHu57dt21bt2rVTWlqaTp48qRMnTqh3797u9piYGB07dkw5OTlKS0tT27Zt1b59e4/23bt3ezU2AAAAYGSur45cLpe+++47bdmyRa+99pocDoduvfVWxcfHKzc3V9dcc43H81u3bq0DBw5IknJychQREVGpPTs7W7m5uZLk0d6mTRtJcrdXte7Jkycl6YJjeysnJ8e9LRXM5uaVxq4vgYEBHv+j4VFz37oY9Q8MNP30hckks5l9XYHXv++xD3yL+vtWY6t/vQXk48ePy2azqVmzZnrppZf0/fffa+7cuSouLnYvN2rWrJnsdrskqbi4uNr24uJi99fGNkmy2+0X7PtC7d5avXq1kpKSPJbFxcW5T0JsKCEhlgbtH5VRc99qyPq3PFXsfhwYYFJYWIsGG6up4vXve+wD36L+vtVY6l9vAfmKK67Q9u3bddlll8lkMqlbt25yOp168skn1adPn0qB1G63Kzg4WJIUFBRUZbvFYvEIw0FBQe7HkmSxWKpd90J9V7R7a+zYsRo8eLDHMrO5ufLzz9aoH28FBgYoJMSiwkKbHA7nhVdAnVFz37oY9T9zxuZ+7HC6Guz92xTx+vc99oFvUX/fasj61+ZgSL0FZEkKDQ31+LpTp04qKSlReHi48vLyPNry8vLc0xMiIyOrbA8PD1dkZKSk8qkSFfOMK6Y6VLRXt+75+q7p1IiIiIhK6+TmFqmsrGHfRA6Hs8HHgCdq7lsNWf+yMsOt9Fwu9nMVeP37HvvAt6i/bzWW+tfbRI8vvvhCffv2lc320xGar7/+WqGhoe6T5lw/3ufV5XJp165dslqtkiSr1aqUlBT3eidOnNCJEydktVoVGRmpdu3aebSnpKSoXbt2ioiIUFRUlI4dO6bs7GyP9qioKHff5xsbAAAAMKq3gBwdHa2goCA9/fTTOnTokDZv3qzExEQ9+OCDuvXWW1VYWKh58+YpMzNT8+bNk81m07BhwyRJ99xzj9avX6/k5GRlZGRo2rRpuvHGG9WhQwd3+6JFi7R9+3Zt375dzz//vMaNGydJ6tChgwYMGKAnn3xSGRkZSk5O1oYNG3TfffdJ0gXHBgAAAIzqbYpFy5YttXTpUs2fP18jR45UixYtdPfdd+vBBx+UyWTSa6+9ppkzZ+rdd99Vly5dtGTJEjVv3lxSebiePXu2Fi9erNOnT6t///6aM2eOu+8HHnhAP/zwgyZPnqzAwECNGjVKEyZMcLcnJiYqISFBY8aMUXh4uObPn69evXq5t+t8YwMAAABGJlfF3APUWG5uUYP1bTYHKCyshfLzzzaKuTj+gJr71sWo/86j+Xo0eY8kqUNosNY90KdBxmmKeP37HvvAt6i/bzVk/cPDL63xOo3jYnMAAABAI0FABgAAAAwIyAAAAIABARmAX+LkCwBAdQjIaHIcTpemvrdPT7y3Tw4nMQfe45RkAIA3CMhocv7v21xtPviDPj/4gz76JsfXm4MmyuTrDQAANFoEZDQ5BbZS9+P8c6XneSYAAEDNEZDRBHHsD3VXMdvCVurw6XYAABofAjKaNOaUoi7mf/ytBidt1dbvTvl6UwAAjQgBGU2OyXAAmXyMmvj56+Vf6dkqc7r03+v2+mR7AACNEwEZTY5xggV3SkdtMVEHAFAdAjKaHBPJBgAANCACMpoczyPIPtsMNHG8dAAA1SEgo8kxGQ4hE3JQI7xgAABeICCjyTEeQXZyCBm1xEwdAEB1CMgAAACAAQEZTQ4n6aE+8NkDAKA6BGQ0OSbDh+PMsAAAAPWNgIymx+NGISRkeI/XCwDAGwRkNDlc5g31gZk6AIDqEJDR5HCradSHrIJiX28CAKCRIiCjyTGJhAwAABoOARlNDlexAAAADYmAjCaNk65QE7xaAADeICCjSeMkPQAAUN8IyGhyOEkPAAA0JAIymhyPG4X4cDsAAMAvEwEZTU6A8QgycyxQA7xcAADeICCjSSPwAACA+kZARpNj4jpvAACgARGQ0aRxABkAANQ3AjKaHOPxY6ZYAACA+kZARpPjOs9XwPnwagEAeIOAjCbHeOWKuh5B/vK7U7r9tW1al36ijlsFAAB+KQjIaNLqekTwT+v2KueMXQs+PlAv2wP4Iy63COCXhoCMJsf4u5jfy6iOw+nS0m1HtGFfdp36IfydX1FxmUYv/0pT1uyhVvBbJWVOOZy8/n9JCMhoclwej/mBhHJn7WXafjhfpQ6nJOnDjBy9+uURPbvxW31fYKtxfy6XS1PW7tHvlu5UwbnS+t7cX4xl24/qSL5N247ka8+JIl9vDnDR5Z216/bXtukPK3cRkn9BCMhocgjFqMpj/9qnyWv36OXNhyRJ6ccL3W1H838MyF68dIpLHXI4Xfo256y2Hc7XsdPFWvKfIw2xyb8IRSVl7sclZQ4fbgngG//YekSni8t0IPes9p4ovPAKaBIIyGhymuqnuJm5Z/Xf6/Zoc+YPvt6UX6Td35+WJK3efVySZLkk0N1WXOpdcMsuLNatr27T+FW7ZTOsc8YQAuHJeOt3Dp7hl2zviUI9kpyuLw+d8lhe8uOnVpJUxpvgF4OAjCanqc5BfvCdVG39Ll9T1+/z9ab4BcslP/14s5U6z/PMn7y69YjO2h36JueMvs09414eEMDdG6tjMlyZnDnI+CW7/61UfXW0QH/6116P5QSpXyb2K5oc4xSLpvTr+Kydj58vJuMRZJuXR5DLDEeCSsp+eswPyuqZOIIMP+f5HuBN8EvBz300OZ5HkPlhhKoFVxGQLzR/3Ww4Umw3hOUAE0eQq2OsDW9H+COTl++BnKISbc7M8/hDHI2X2dcbANSUx1Us+IWMahinWBR7OcXCHPjTOnbDEeTGno9drvLo74sgb5x9wgm08EfGd935fieNWLpDpQ6XHux3pSb179jQm4U64ggymh5XlQ8BD7WZYmE8glxS9tOrK7ARz0F2ulx66J00jVy284InE7pcLr2y5Tst2Xq43j59MR49Y4pFwykpc2rb4VN8atYIeXyK8rPfSmvTjmv2xm901l6mUkd52+vbjl7U7UPtcAQZTU5TPErFVRAuvkDDLy1bqUPr0o7rxc8OnXed6qZYNN54LH11tEBpP17S7p9ffa9HznNk6svvTmn59ixJUtQVl6nPVWF1Ht/z6Fn9vTdziko07+Nv1f9XrTUmul299dtUDXh5iySpXUiQ/vVgH6b9NCImj09RfmIrdeivmzIlSc2bBQpNC0eQ0eQ4m+Ac5MawmaUOp+Z99K2Wbz+qo/k2vfjZQR364exF3Ybk1OP695663dnO6H+2fKfRy3de8PuwlTm1YFOmisuqn2qx40i+UrJOu782XtN3TdoJfZNzpqrVamTb4VPq/fzneu3Lw3Xuq4Lxe7Jd4ETQ73445358+NS58zzTew11kt7sD7/R1u/ytfCTzDr3lVNUorRjp5vMz4vzOV5Yov9et/fCT8RFU90UC+M0LW6i0/T4TUAuKSnRjBkzFBsbqwEDBmjZsmW+3iTUkvFX3Jq0EzVat7jUoZSsAo8rFFwMlwR6Hu05bbt4d2bLPVOix/61V6OWf6X39mTrlS2HNXLZTr2Vckz3rEi5aNux40i+Ev8vU3M++lb7suv+y2LBxwe0bHuWDp+y6aWfHRn+eRDy5jrIcWv2KDPvp6D980vDPfB2ah22ttyUteXBpj4/YjV+rxc6qGicKnK+MFtc6tAXB3/w6pMPz5P0Knda209P9mfX/Q8Sqfy6tHf8Y7sefCdNO44U1EufvrbtcH69h/3cMyVKySrw6Pf46WLN+n8Z2nb41HnWrF5xqcOr9972w/m6980UffJtbq3GqQ/HTtv01dHy7//UWbve3X1MOUUlXq3r7Ul63vq+wFaru3+ifvnNFIvExETt3btXK1as0PHjx/XUU0+pXbt2uvXWW329aaipOvwEev7Tg3pvT7auDW+hhXd2r8eNqpnMvLOK6RBabXuZw6nAAJPHD97amvvRt9r6XX6VbT8PSWftZWrRrH5/LOQUlejTA3nKOfPTL5tvcs6ox+WXerW+y+XS8u1ZcsmliX2vlMlk0tF8m9al//THUUqWZ/D5+R9A3s5BPt86F/uPKm/V5O0Q4DFf2CWXy6VSh0vNzJ7HSireJzd3bqMF/3X+90l1NwpxOF36YP9Jzf7wW42Nbqepg6/xfkN/xuVy1fq9kF1YrB+nfmrZ9qPq27Hu00outqrCcIGtVEu3HdV1bUN0a7eIOo9x22vbJUlzb+uqW7pFyOVy6c7Xd0iSvsk5q34dW9WovzMlZbpr6U6ZTNK6B3qf9+fK5LV7JElPvf+1dj4RXsvv4MKcLpee+d8MOV3S3Nu7uv9gLHU4NeL1nZKknp9/pz3Hyj9JWvXV91r/UN9q+ysqLlOLoMAqT1Q9bSv1+Bm1/2cHBbLybeoQZqnUZ05RiX63tHxb3n+ojy4PCb7g91VcWn7t9h5tQzymiVXVd8sgc71N9ygsLlVOkV3XhLeol/4aG78IyOfOnVNycrL+8Y9/qEePHurRo4cOHDigVatWEZANfjhrV9IX36n/r1rp5i4N90Oqrn5+9O2zA3nq2zHM46SsqjhdLn16IE+SdCD3rMav3N1g23ghf3w3XTuf+E2l5e/vLT/Cm3fWru6XX6rl90bVea7hhY7E7TiSr7TjhTpZVKL392br8Rs7aez1V5x3naP5Nj3+r72Kan+Zjp8uVpsWzTR1cCeFBF/i8bxSh1P3v7VbOWfsHsvLHN6nup1HC/Q/P05J6HH5perXsZXH7Y0lyf6z/s6VOjyuO13dHwjnU3FnvppyuVzamJGj9pdZ1LNdSK36qGAvc8ocaDrva8D4nQeYTNp2+JQy887JHGBS9BWXqUtkyyrXe/GzQ3rxs0MKMgfo76N6KsYwH/m9H6fBbPo2T1dvPaJjhcW6tk0LOZwuDe0arshLg9yB1RhcV+zIUoGtVM/9n+e0iNW7j3sEZG8Cr3Ef3/fPXfrn769X/jm72rQMOu96P3eisNj9uG1IzdatqVKHU//c+b1CLWbdZa2/edMnqziSOWXtXn2Tc0ardx9Xx1YWdY2s+g/OwuJSfffDOfVsF1Ll6yjjZJF7f0vS0x9k6ERhsb4w3C0uM++spqzZo8cGXa0rw5or0PTTfi8udSjIHFBpf/57b7byf/yk7M2d3yvy0iD1uTJUweaA8+7DUodTr//niEwmk7pffqkGXN2qyu329o+mouIyvbz5kGKuvEwul/TRN+VHqW+8prVu+fEPix/O/vTzqSIcS+XTWeZ8+I1mDOlc6UTd8at2a392kfr/qpXah/4UYiv+SHz6fzO07Uj1P3eeWL9P706IrbT8s8w89+P/+zZP98W2v+D3uGDTAX2wP0fDe0Tq6aGVt1WSDv1wVvesSFHkpUFaN7G3xxV7LiQlq0B/fDddf+x/lR7od5Wk8vqPX7Vb3xcU6zedWuuhG65UYIBJzQ8Wxt0AACAASURBVAIDdFWr5l733Zj5RUDOyMhQWVmZoqOj3ctiYmL06quvyul0KiCg8c80cbpcKrSVyeFyyen6/+3deXhU5fn/8ffsk8kyZCVhSQghbCGBsO8iZROtVX/2S1VApSIK1qpUgRYsWqt1qwtYFdHSAqUaBIS6gDZgkM2SsATCGgIhAZKQhIQkk5nMzPn9McwwEVCUwCHJ/bouL2QmGZ7zOcvcc859nlFwuRXciudxtwJut4JLUdBqNASbdOi0Gt/ziuI5y6PRaHx/6jQaNBrPG6r3sb98dYgNh0v5z94iAIYkhFPncuN0Kxwvt7G/uIrWVjM9Wls9ZzfPjc17jPLtkt43zu8871Y8Z0adbs/4nW6FOpebvLIaHE4FvVaDXqs5d+YU33K6FM/ZKLfbs+wl3ym2nlydA8Ckfm0JMukJNOrO/amn3OYgKTqEqGAjW/LKqag9/6br//8AuSXVdA8JoMbh5JvcMtYfOs29fdoSZNKh12nRazUYdBoMWi3FVXa+PVZOiNlA6xZmWlvNBJv0uM4to8s7drdCaU0dBeUXXi7bcOg0ZoOWAIMOs16HyaDl2bUHfc/nnDrL+MVZ9IltQXSImZhgEzEhZqJDTISY9Wg0GhRF8f2bznOZOt0KTpebx1buIff0D/eZTlueXe/vr6zPZc3eIiYPiGXL0XK6xQQzMD4Mg1aL4Vyx9v8+8JzhOOa3XJ/vK+Y/D/bzbDMuBYfLzcKt+RcUxwAvpx/m5fTDvH9XD9qHW6h1ejI7UmnHWVtHXIsA0g+dJvP4GYJN5w9Tn2Sfon+7MNw/0Oz68a6TLNh87AeX/ftc7Itd/p1VSJ3LzU1dW3KwuIq80hrcisKbGXkXfY0uLYOY2KctvWNbYPnOB7iCMzZW7j5FXGgA7cItWAw6quxOqhxO/vntcfYXV/naPGaO6IDDpbDpSCnVDhcP9I8jxKzHGmCo166zZHsBS7YX1Pt3nr+lCyPPfeB9dX3uBWO0O91MX7WXZff3JjQ0kOrvfPhYsKV+jvM2epb12bGdGNgurF67zP7iKvb/9+I9w1V2J25F4Z5/ZnHqrJ2hCeE8c1MnTp21E2TUEWzWc6C4isSIIMyG+sfkQyXV9H9tIwBzRndkbNeWaDX4jn21Thdvfp1H29AAhidGYNBpMOq1aNEwz2/dlFQ5qLI7OWt3cqKiluPlNixGHS0CDGw8UsaGQ6cJtRjo3y4Ui0GHxagjwP9Pgw6zQUu5rY4OEYFE+hV6dqeb//fB/3zFbHGVg55trLSymtFpPfuN90yw9tzxV6PR4HC6OVhcxcmzdtyK4msXGp4YQZsWAYQHGiitvrAly78n/qnVObw7rrtvvyupsrP7RCUnK+2s21+Mw6Xw4MA4kqKDOVhcxYHiaqocTkqq7Bc9Rrz1zdELHtt6rJxxi863ZHWICPS1JCVFB/Pk8ASKqhwUlNv4NKeII3797h9856TGc2M7c0OHcOpcCnXu+ldmBr7+Tb2/PzIkngl92rDhcCkzzh3rvcZ0ieJXPVtTW+fCqNNyqKSKFgEG+rUL9WQMzP5sH5vzyvlkT/17Hz7fV0xYoIGSKgd//PzABcvrtXpPES0CDL7C0Mt7RnhTXv32k9XZp3h/a/4FZ4y/K6+0ho92FPLvrEJGd45iXGprqhxOvj5c6vuZOpf7kh8EjpRW897mYxwrt3GoxLMe/rO3iP/sLWLTbwd7ti8AjYbTVXbfujtZaWf94VISIwIxG7T1imm7001Gbilf7Cvmvn6xtAoxsbOw0nfceGfTMbYdO8Od3WMYGB9GwRnPh8+M3FIycs+Pe9m9vWht9Xxo8B+5dzl0Gn5Uga4WjdIU7lr4AWvXruXZZ59l06ZNvsdyc3MZO3YsW7ZsISzshy8dFRcXU1JSvz9Kr7cQFXXll7YuRqfTEhISQGWlDZvdyV2Lttc74Igr88zYzrz81SGqGum322k1nkvrTX7nFeI6Vu/mLNVGIZq6754Q1gA/4iLcdUengQcHtePB78y441/3uBr4y1RCQ398G0izOINss9kwGo31HvP+3eG48OzWxXz44YfMnz+/3mPTpk3j0UcfbZhBXkJISACuaofvk5q4tBYWA2drnbgu41b6e4cmMKhzFCP+mnENRvbTxVjNuNzKBTeLyHyzQlw9YYFGqu3OH+w7b8jdUKOp309u1GkJCdBTVu1o0P1dr9XQISoIvU7DnsLKH/z5nU+PpMezXzbcAMSPdj0d79uGBXC87MpuIHQpsKeo6pJFa0jIhb3ZamgWBbLJZLqgEPb+3Wz+4QZ4gHHjxjF8+PB6j+n1FsrLr840Wf6fpHC5+fD+3uScOotWg+9ynVarQedrk9Cg1UKV3UW13em5vHiufcLrfEuGpy1D8WvPcOP5c1NeGRmHS7m3b1vahAZg0GrQ6zSY9DrCAw0UnqnF4XL7itB6+63CBY95L1AoeOal1fm1Uei1GiprnWw5WsbwjpGktArxtV4oiuJbTt25n9dqPL9Tbqtj9e6TdI4OxqzXknPqLLelxBAXZkFRFOxON1V2J9UOFycraimpdvjGm32ikvv7x1JeXk24QcuX0wbw7BcHGNEpigCjDpsbFKeLsmoHW/LKuKt3Gzh3I1Od29M6UOdW0AAD4kMBDYVnbBScqcVW5/Itm/9yBhh07C86y+lqB/87doYwi4EHB7UjLsxCbZ0LW52LWqeb2joX+4uqOFxSza3J0YQFGukU5ekfdTjdFJ21c7KylpMVtVTWOuu1zXhbQHz/6bS8800eR07XsHJyXxZsOkqt083uwkpO+/XbvXxbEm1DA0iMDOSf3x7n7Y15/DK1Nb8eEEtlrZP1h04TYNBhNeupcyvUOd3UuRVCzHpcbk+7S2VtHWdrnWQVVDB1SDxGnQbDufGs2XOKwyXV3NItmpfPXXrXavDcFKbA5EFxhFuMmPRaLCY9p6rrqLE5MOu1VNQ6qTh3Kdv7LXEuRcHmcGHQeW7UcysK/eJC2VVYiUGn5dTZWvrFhWKrc6EoeF7XqGPtvmLMBh0nKmrpGh1MfnkNG3PPXxptFxZAfrmNLtHB9GxjRQE+zCokKTqYFhYDm46U+Sb5T4wMpFdsC7q0DCbIpONAURVnbHX0axfKgaIqDpZU0SkqiHc3HSPMYqCspo6ZIxPRauDznGJ2FFTQs62VuFALkUFGbu7WktzTNVgMOoJMnvagb4+V+/a1AIMOu9PNiE6RBJv05J+x0T7cwhlbHZW1Tspr6sg9Xc0/v/XMb9zKasbtVogNC6C02sEt3aIxnLvh8/UNub7l6NXWSpfoYO7u3QZFgS1Hy9HodTjsdbjdCrY6F9YAA/87Vs66/SU8MCCOhAgLW/LK2X2iggcGxqGca3+al3GEn3eLJsik53S1gzM1dbQMNtHKauaFLw/xi+Roera14nAp5J2u5l+ZhSREWBjfpy0aPHNO67QaCs7UUuNw0T7CAopnppLhHSMY3D6cELOejbmlpLQKweFy+7YJrcZzfHn1v4fpHRvKr3q1xuF043C5qXO5adMigK7RweSV1lDtcHKszIbd6aZ9hIUuLYPZd+osp87acZ7rr/bvo3e63djq3NgcLmocLmrqPH8WnLFhq3PVa/U5e66gHhAfxtGyGo6X22gXZiEiyHiuBc5z5k85d1z0XgHSAO0jLMSFWahzw8EyG1aDhvhzfZwut0K1w0nJWQdr9xfz/pZjBJn0dG8dQmyohTV7TvHkzzoQaDzfChZk0lNwxsaR0zWktA7hbK0TjQbahVlIiAj03ZSZc+os874+gltRiAuzkLbjhG95buoaxezRHVHsdSyf1IdnvjiATgM7/Ypqo07Dh/f34UBxFcEmPW1DA4gOMfH8uoOs2n2KlFYh7D43R/eozpHcmBjBWbuTtB0n6BgVRN+4FrgV2FlQwdr9xTw7tjP55TYWbjnm+/bL2aM7Yne6Katx8P6WfDpFBfHE8AS0Gk+7gHd5WwQYSD9Ywpa8cn7Vq7UnX8WzHb+Snkuvtp7WwGCTHludi0Htw+kWE4wCPJK2m1+mtsblVuiTEIG9to5qex37i6pYk32KBwbGEWzW43QpvPDlIQCCjDocLjdJMSHc0CGcQyXVVDuc/PaGBFq3MPPVgRIWbDrKPX3asjG3lL0nKxnWIYJQi5HT1XbahVkYkhDO//LP8O6mo5h0Wm5LiaFzdBDvbjqKUafllm7R2OtcFy2QK2rreH9LPhFBRk5/p41t5sjEetuaTquhc8tgTlc7KK12YDV7btSzO931TihpNZ7WpJ0FFcSFWbAYPcc3UFi4JZ/lk/oQH2Fh29Fy8kprsNW5KK+p853A+fJcf/eTP+twvk3q3Hbu/VcURcGg0zK0Q/gF9dP1dga5WbRYZGVlMX78eHbv3o1e7/lMsHXrVqZMmcKOHTt+cg9yScnVm9dQr9cSGhpIeXk1zuv07vmmRjJXl+SvLslffbIO1CX5q+tq5h8ZeXmzJvm7/rukG0CXLl3Q6/Xs3Hl+HtPMzEySk5MbxQ16QgghhBDi2mkW1WFAQAC33XYbc+fOZffu3Xz11Vd88MEHTJw4Ue2hCSGEEEKI60yz6EEGmDVrFnPnzuXee+8lKCiI3/zmN4waNUrtYQkhhBBCiOtMsymQAwICePHFF3nxxRfVHooQQgghhLiONYsWCyGEEEIIIS6XFMhCCCGEEEL4kQJZCCGEEEIIP1IgCyGEEEII4UcKZCGEEEIIIfxIgSyEEEIIIYQfKZCFEEIIIYTwIwWyEEIIIYQQfqRAFkIIIYQQwo8UyEIIIYQQQvjRKIqiqD0IcaHi4mI+/PBDxo0bR1RUlNrDaRYkc3VJ/uqS/NUn60Bdkr+6rrf85QzydaqkpIT58+dTUlKi9lCaDclcXZK/uiR/9ck6UJfkr67rLX8pkIUQQgghhPAjBbIQQgghhBB+pEAWQgghhBDCj27u3Llz1R6EuLjAwED69u1LYGCg2kNpNiRzdUn+6pL81SfrQF2Sv7qup/xlFgshhBBCCCH8SIuFEEIIIYQQfqRAFkIIIYQQwo8UyEIIIYQQQviRAlkIIYQQQgg/UiALIYQQQgjhRwpkIYQQQggh/EiBLIQQQgghhB8pkIUQQgghhPAjBbIQ4pqS7yZSj9vtVnsIzZZs90I0LlIgq6CsrAyQA+a1VFVVpfYQmrVFixbx3HPPAaDRaFQeTfOTlpbG1KlTZT9Qkd1uV3sIzVp5eTkg77tqaYx1j17tATQXiqKg0Wh47733KCsr47e//S1ms1ntYTV527dv57XXXsNsNtO1a1duv/122rdvj9vtRquVz4dX27fffsucOXOorq5mxowZag+n2dm2bRtPP/00+fn5AFRWVhISEqLyqJqXrKwsXn/9daxWK126dGHMmDG0b9/e954grh5vxu+++y7V1dVMmzYNk8mk9rCajcZe90iFcI24XC4Atm7dysaNG8nKylJ5RE3f+vXrmT59OgMGDGDAgAHs3LmTl156CUCK42vgT3/6ExMnTuTOO+/kyy+/5Oc//7naQ2o2iouLmTRpEpMnT+b2229n3rx5JCUl0aZNm0Z1Bqex2759OzNmzKBXr16kpqaSlZXFpEmTKCoqkuL4GvB/392wYQM7duxQeUTNS2Ove6RKuEb0ej1VVVUUFBRQVVVFeno6p0+fBhrXJYfGwLtTbt++naSkJB555BEeeOABhg8fjt1up6amBpDcrxZvn6vJZKJr165MnjyZgICAC35O8r965s2bh8vlYu3atTz00ENERUVRVFTEiRMnpDC7Brz7QFZWFkFBQTz66KNMmjSJhQsXYrFYeO655ygtLVV5lE2f//tuRUUFGzZs8OUux5+rr7HXPVIgXyWnTp264LEVK1ZgtVqZMGEC2dnZbNq0CZCezIbizVyn0+F2uykqKqJPnz6+5/fs2YPBYODIkSOA5N7QvPl7z86PHDkSgF27dgGQnp7OkiVL2LJlC263W/JvYP7HnD/84Q/84x//ICYmBoCioiKCgoIIDw9vFG9MjZX/PqAoCgcPHiQhIQGNRuMrmp988knS09PJyMigrq5OzeE2OUePHgXq34y6fPlywsPDmTBhApmZmWzduhWQ4//V0NTqHt3cuXPnqj2IpuTgwYNMnTqVtLQ00tPTCQgIICEhAYC8vDyCgoKYOnUqX3/9NYWFhSQmJhIaGir9aFfgu5kbDAY6duxIbGwsnTp1wmq18uabb/LZZ59hMplYtGgRmZmZ9O3bl6CgILWH3+h9N3+z2UyHDh3Q6XTs37+fY8eO8emnn/Lxxx9TUFDAsmXL2LlzJ/369cNisag9/EbvYsecTp06AeB0OtFqtRiNRt5//32GDRtGVFSUHG8a2KWOQcXFxaxYsYJf/vKXmEwmNBoNO3fuJCMjg9raWvr16yfHoAaSlZXFk08+ybBhwwgJCfFt+3l5eVitVh5++GHS09M5efIkHTt2pEWLFrIfNJCmWvdIgdyA7HY7Tz/9NG3atGHSpEnU1NTwt7/9jcjISBISEujUqRODBg0CIDQ0lM8//xyTyUT37t2v643kenaxzN955x0iIyPp27cvYWFhAAQHB3Pfffdxzz33cMsttzB//nwCAwNJTU2V7K/A923zvXr1Ii8vj4yMDMxmM/PmzeOOO+7g1ltv5c033yQgIEDyv0Lfl3/79u0xGAwAlJSUkJ2dTceOHYmPj5fMG9CljkFRUVHcfPPNrFixggMHDhAeHk54eDh///vfufXWW1m1ahUpKSkkJCRc94XC9cx7NWrt2rWsXr2awMBA+vXr57uSFRsby5AhQwDP++6nn35KYGAgycnJknkDaMp1j8xi0YDy8vLIzMwkLS2N+Ph4Bg8eDMCaNWsICAhg9OjRKIqCoigMGDCA5ORkNm/eTPfu3UlNTZWD5E/wfZmbzWbGjBkDQNeuXQFPf3Lbtm2ZOHEiaWlpPPDAA6qNvSm4VP6ffPIJkZGRjBkzhrfeeosxY8b4Lu8HBQVx//33s3z5ciZPnqzyEjRul7v9d+jQgZKSEk6ePAl49gOdTqfauJuSS62DVatW0apVK9555x3mzJnDE088QVlZGb179+b+++/n6NGjpKWlMXLkSDnuXwFvIZyZmUlcXBwZGRkMHDiQ3r1743a7CQwMBDyF9KBBg1i7di0bN24kJSWFlJQUed+9Qk257pEe5AYUFBRETExMvZsvHnjgAUJCQli3bp3vzmXvTWQTJ06ksrKSr7/+murq6ut2I7mefV/mX375JUVFRcD5OUi9RUFSUhKA3ChzhS6Vf4sWLVi+fDkRERGsX7+eKVOmoNFofNt4cnIyTqeTkpIStYbeJFzu9g+envA1a9YASHHcgL5vH/jXv/5FeHg4S5cuZf78+axcuZJFixYBYLVaCQoKwuFwqDTypmPnzp0cPHiQhx56iPj4eJYuXQpcfLai++67j7KyMjIyMqipqZH33SvUlOseKZB/gksd0BwOB+Hh4ezdu9e3MYSHhzN69GhKS0vZsGEDAAaDAbfbTXx8PCNGjGDz5s18/fXX12r4jdKVZr5s2TLWr19PRUUF4JkCrmfPnoSHh1+T8Td2PzX/1atXExYWRn5+PtnZ2b7XSU9Pp3fv3kRGRl6zZWjMrnT7B+jduzd6vZ7MzExAvlXvx/op66CsrIzPP/8cjUZDmzZtMJlMVFZWAnDkyBHi4+MxGo3XbBkas+/7IBEaGkrnzp35xS9+Qb9+/SgsLPR9GPRu594bJ9u3b8/IkSPJyMjw3TAmvt/33djblOseKZB/hLq6Op599lnmz58PXPgG0759e2JjY9m6datvYn7wnLmJiIhgz549nDlzpt7v/OpXv8JqtWK1Wq/+AjRCV5p5dnY24PkWn1mzZjF58mQefvhh1qxZw89+9rNrtyCN1JXkHxUVxd69e7Hb7SxdupR77rmH3/zmN0ydOpVVq1YxYsSIa7osjVFDHHO832AVGxuLxWIhPT0dRVFkLvDLdCXrIDIykpycHBwOB1u3buWJJ57gqaee4pFHHiE7O5sbbrjhmi5LY/RD+SuKQlxcHG+88QZarZYhQ4bQpUsXVqxYQVVVla8w9nfXXXcRFBQkN0heBrfb7St8vX/315TrHrlJ7zItXLiQKVOmkJWVhdvt5s4776x3acDhcKDT6WjXrh0ffPABERERdOzYEb1ej1arRaPRsGTJEsaPH4/RaPRN+2Mymbjpppto166degt3nWqIzBcvXszdd9/NkCFD6NWrF8HBwYSEhPDyyy/72izExTVU/hMmTGDYsGHExcWh0WgICQnhtddeo1u3biou3fWvIY85JpOJsLAwdu7cSatWrUhOTpYC+TI01D4wfvx4unTpQqtWrbDb7ZjNZl555RU57v+AH8rf5XLVa91yOp2EhISgKAqZmZlUVFTQu3dvAN/PuVwuzGYzY8eOJS4uTpXlaiz+8Y9/8Je//IXNmzdTWlpKSkoKGo3G1zfc1OseuUnvB/z3v//lhRdewOVy8de//pVt27b55q50uVxotVrcbrfvMll8fDy33XYbn376KW3btvWdIUhOTqa6uprjx4/TpUsX4Hx/lPdOc+HRkJnbbDYOHz5MUlISqamppKamqrZcjUVD5l9TU8OhQ4fo1q0bY8eOZezYsaotV2PR0MecgoIC3zFnxowZckn/MjT0PpCbm0tSUhKDBw/23cQkLu1y8/f20peUlBAZGel7T+3bty+7du1iw4YNjB49mnbt2vluTPX+jrzvXpqiKLzxxht88cUXvg8oH330ES6Xi3vvvdd3Frmp1z1yCuF7VFRU8Morr3DzzTezbt06hg0bhk6n811G0Ol0aDQadDodJ0+eZMKECdxzzz2MHz+eyMhIli1bRkZGBuDZ4Tt37uybG1Bc3NXIPDExUc1FalSuRv4dO3ZUc5Ealat9zJHi+IfJMUhdPzb/e++9lylTplBbW+trpwgKCuLGG2/EYrEwb9483++JS/NvQ3G5XOzatYu77rqL22+/nRkzZhAXF+e76df7QaOp1z1yBvkiioqKaNmyJVarlVWrVmEymXzPlZaW+m4sqqurw2AwsGLFCp577jluvPFGpk6dSlhYGNOmTWPlypVMmzaNrl27sn//fqZPny5vUJcgmatL8leX5K8+WQfqupL8n3nmGcxmM3D+G9q6d+/OkCFDfNO8Xc/TiV0PiouLadmype//bTab74ucgoKCOHnyJAMGDKCsrIywsDCWL1/O888/36S3f40i3ztaz9tvv82ePXt466236j3uvTyzaNEiPvroIz777DPfc5s3byYwMJDu3btf8Hq7d+/m2LFj9OnTh+jo6Ks+/sZIMleX5K8uyV99sg7U1dD5e4thme/78lws/5kzZ7Jt2zYSExPZsWMHwcHBKIqC0Wjk6aefxmQyYTQaSUlJueD1msr2L2eQv2Pz5s3k5eWxceNGhgwZgtvtRqvV+nay4OBgoqOjKSwspHXr1gAMHDjwgtfx7qDeycjFpUnm6pL81SX5q0/WgboaKn8v75liKY4vz3fzB8/9Clu2bOGtt95i7NixPP300xQWFpKWlsb06dP5+OOPfevCq6lt/9KD7KeoqIhjx44RExPD4sWLcTgcvp4m74n2xMREdu3ahV7v+WxxqRPwcinn8kjm6pL81SX5q0/WgboaMn/x410sf/DMLT1q1ChSU1MZPnw4Op2O2NhYpk+fjsVi4dNPPwXqT/vW1Lb/Zlsg5+bm+iZs9+6Iq1evJjY2lnvuuYeamhqWLVvme947tUlKSgoxMTF88sknvufE5ZHM1SX5q0vyV5+sA3VJ/ur6MfmDp/hdv369L29v8ZycnMzx48eBi39bYVPRdJfsEjZs2MDQoUN57LHHuO2221i3bh0VFRVoNBqMRiOjR49mxIgRJCcns3btWk6ePOmbUsY779+oUaPIzs6+5CTkoj7JXF2Sv7okf/XJOlCX5K+un5I/eGa9ueGGG3jppZew2+0YjUaqq6spLCxsFtMVNosvCvF+Eq2trWXOnDncdNNNPPHEE9hsNr766itOnDjBwIED6dGjB927d8doNGIwGNi9ezcFBQUMHjy4Xk9TcXEx27dvJyQkhMTExCZ3WaEhSObqkvzVJfmrT9aBuiR/dTVE/gApKSmkpaXx0UcfkZOTw4IFCwgICGDChAkEBwervJRXV5M/g+x0On070r59+ygsLOTOO+8kNjaWGTNmMGbMGL755hu++OIL4PwlhF69etG/f3+2b99OVlYWgG+i8kGDBmG1WomIiFBhia5/krm6JH91Sf7qk3WgLslfXQ2Rf2ZmJgCRkZEsXLiQ+++/H7PZzB133MHSpUuJiYlRZ+GuoSY9i8V7773Hxo0badmyJUOHDqVv375UV1djs9l8PzNy5Ejy8vJYsGABo0ePxmg04nQ60ev1DB06lOzsbJYtW0bPnj0xGAw4nU7CwsJ488035Q7Zi5DM1SX5q0vyV5+sA3VJ/upqqPz//e9/06tXLwDi4uKIi4trdnNJN8kWC7vdzu9//3s2bdrEuHHjOH78OJ999hlFRUV06tSJffv2+aYyCQwMxGKxsGnTJmw2Gz169EBRFLRaLREREdTU1LBu3ToCAgLo0qWLryG9KTem/xSSubokf3VJ/uqTdaAuyV9dVzN/r+ZUHEMTLZCLiopYunQpM2fOZPTo0dx4443o9XqWLFlCt27dOH78OO3atSMqKgrwzLFYUlLCzp07ueGGGwgICPDdHGC1WrHZbKSmpjaLSwo/lWSuLslfXZK/+mQdqEvyV5fk3/Ca5Mex/Px8cnJy6NOnD+C5EzM0NJTWrVuTkJBAXV0da9as8f18cHAwnTt3xmazUVNTA5z/pNq6dWsef/xxUlNTr/2CNCKSubokf3VJ/uqTdaAuyV9dkn/Da5IFckpKCsOHDyc/P983FYzRaOTw4cOMGjWKAQMGsHfvXt+cigCtWrVix44diQ4XcAAAB0RJREFU9Sa9FpdPMleX5K8uyV99sg7UJfmrS/JveE3yJr3AwEBefPFFLBaL77Hs7Gzat2+P1Wpl7NixnDlzhldeeYWoqCgSExPZvHkzQ4YMITw8XMWRN16Subokf3VJ/uqTdaAuyV9dkn/Da5IFMnDB/Hw7duzwfTd4dHQ0Dz30EGfOnGH27NloNBrOnj3Ln//8Z0wmkxrDbRIkc3VJ/uqS/NUn60Bdkr+6JP8GpjQD+fn5Svfu3ZWVK1f6HquqqlIURVHKy8uVjIwMtYbWZEnm6pL81SX5q0/Wgbokf3VJ/leuSfYgeynn+nBycnIAGDFiBAALFiygV69erFy5khYtWvimPhFXTjJXl+SvLslffbIO1CX5q0vybzhNtsUCzs/Zd+jQIbp06cJXX33F/PnzcbvdvP3229x4440qj7DpkczVJfmrS/JXn6wDdUn+6pL8G06TLpC9dDodO3bsYP/+/Tz88MM8+OCDag+pyZPM1SX5q0vyV5+sA3VJ/uqS/K+cRvGej2/C0tPTOXDgAL/+9a8xGo1qD6dZkMzVJfmrS/JXn6wDdUn+6pL8r1yzKJCVZvb94dcDyVxdkr+6JH/1yTpQl+SvLsn/yjWLAlkIIYQQQojL1aRnsRBCCCGEEOLHkgJZCCGEEEIIP1IgCyGEEEII4UcKZCGEEEIIIfxIgSyEEEIIIYQfKZCFEEIIIYTwIwWyEEKI75WZmcn27duv6DVmzpzJhAkTGmhEQghxdUmBLIQQ4nvdfffd5Ofnqz0MIYS4ZqRAFkIIIYQQwo8UyEII0YhUV1fzpz/9icGDB5Oamsr48ePZs2cPK1asYOTIkTz33HP06tWLqVOnApCbm8vkyZNJTU1l8ODBTJ8+nZKSEt/rVVRUMHv2bIYMGUJSUhIDBgxg9uzZ2Gw2ADp16gTArFmzmDlzJgBFRUU8/vjj9O7dm379+vHQQw9x9OhR32sqisLf/vY3hg4dSo8ePZg1axZ2u/0aJSSEEFdOCmQhhGhEHnvsMTIyMnjhhRdYtWoVbdu2ZdKkSVRWVpKfn09xcTGrVq3i8ccfp6ioiLvvvpu4uDiWL1/OO++8Q1VVFePGjaOmpgbw9Abn5OQwf/581q5dy6xZs1i1ahUffvghAN988w0Av//97/nDH/5ATU2Nr5d4yZIlLF68mNDQUP7v//6PoqIiABYsWMDChQt56qmnWLFiBSEhIXz22WcqpCWEED+NXu0BCCGEuDxHjhwhIyOD999/n8GDBwMwd+5cQkJCsFgsAEydOpW2bdsC8PrrrxMdHc3s2bN9r/H666/Tv39/vvjiC+644w4GDRpEnz59fGeK27Rpw5IlSzh48CAAkZGRAAQHBxMcHExaWhqVlZW8/PLL6PWet5A///nPbNu2jY8++ohHHnmExYsXM3HiRG655RbAc/Z527Zt1yAhIYRoGFIgCyFEI+EtWnv06OF7zGQyMWvWLFasWAFAu3btfM/l5ORw6NAhUlNT672O3W4nNzcX8NyAl56ezsqVKzl69CiHDx+moKCA9u3bX3QMOTk5VFRU0KdPn4u+Znl5OSUlJSQnJ9d7vkePHr5/UwghrndSIAshRCPhPWP7fcxms+//3W43/fv3549//OMFPxccHIzb7WbKlCkcOnSIW265hbFjx5KUlMScOXMu+fput5v4+HjefvvtC56zWCxoNBrA04f8Y8cuhBDXC+lBFkKIRiIhIQGA7Oxs32NOp5Phw4dTXl5+wc8nJiaSm5tLTEwMcXFxxMXFYbVaef755zl48CD79u0jIyODN954g9/97nfceuutxMbGkp+ff0GB69WxY0dOnDhBcHCw7zVbtWrFq6++yv/+9z9CQ0OJiYkhMzOz3u/t2bOnAZMQQoirSwpkIYRoJOLj4xk1ahTPPPMMW7duJS8vjzlz5lxyhoi7776bs2fP8rvf/Y79+/ezf/9+Hn/8cbKzs+nYsSMRERHo9Xo+//xzjh8/TnZ2No899hglJSU4HA7f61gsFl/7xK233orVauXRRx9l165d5ObmMnPmTDIyMnx9zJMnT2bp0qWkpaWRl5fH66+/zu7du69JRkII0RA0yqVOEwghhLjunD17lpdeeol169bhcDjo3r27byaKWbNmceDAgXo/n5OTw6uvvkpWVhY6nY6ePXvy1FNP0aFDBwDWrFnDvHnzOHnyJJGRkQwbNgyDwUB6ejpffvklAPPmzWPhwoUMGDCAd955h+PHj/PSSy+xZcsWXC4XSUlJPPHEE/Ts2dP37y5evJi///3vnD59miFDhhAeHk5eXh6LFy++dmEJIcRPJAWyEEIIIYQQfqTFQgghhBBCCD9SIAshhBBCCOFHCmQhhBBCCCH8SIEshBBCCCGEHymQhRBCCCGE8CMFshBCCCGEEH6kQBZCCCGEEMKPFMhCCCGEEEL4kQJZCCGEEEIIP1IgCyGEEEII4UcKZCGEEEIIIfxIgSyEEEIIIYSf/w81Ifc/+XQ3KAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104f63710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set.plot(x='created');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Youtube Likes <a class=\"anchor\" id=\"youtube-likes\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10db4a128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({\n",
    "    'created': data_set.created,\n",
    "    'youtube_likes': data_set.youtube_like_count,\n",
    "})\n",
    "plot_data_set.plot(x='created');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Youtube Dislikes <a class=\"anchor\" id=\"youtube-dislikes\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105f28518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({\n",
    "    'created': data_set.created,\n",
    "    'youtube_dislikes': data_set.youtube_dislike_count,\n",
    "})\n",
    "plot_data_set.plot(x='created');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Youtube Favorites <a class=\"anchor\" id=\"youtube-favorites\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5W49zYC3yt1ZV5h8SEnDWt3Fpqa1Tp46ys7NVXFwsT8+yQ9tsNvn6+io4OLjC2CNHjjhtO3LkiCIiIvTZZ5/p+PHjSkxMlCSVlJRIkmJjY/X444/rxhtvrNR8IiIiKiw1sNlOqLi4ap/4JSWlVX4fcEbm1iJ/a5G/9TgH1iJ/a1WX/F1aalu0aCFPT0+lpaUpPj5ekpSSkqJWrVrJ3d15vUVMTIxee+012e12ubm5yW63KzU1VaNHj9a1116rG264wTE2PT1dEydO1MqVKxUaGurKKQMAAKAGcOnKXj8/P/Xp00fTp0/X1q1btXbtWi1cuFCDBw+WVPaubUFBgSSpZ8+ejmvQZmRkaObMmcrPz1evXr100UUXKSoqyvFfnTp1JElRUVEKDAx05ZQBAABQA7j842qTJ09WdHS0hgwZoscff1zjx49X9+7dJUkJCQlavXq1JCkwMFCvvvqqUlJS1LdvX6Wnp2v+/Pny9/d39ZQAAABQw7nZ7Xa71ZM4n2y2E1V2bE9Pd4WEBCg7O7darC25EJC5tcjfWuRvPc6BtcjfWlWZf3h40FnfpnpcWAwAAAA4B5RaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AAAAMB6lFgAAAMaj1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsAAADjUWoBAABgPEotAAAAjEepBQAAgPEotQAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AAAAMB6lFgAAAMaj1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsAAADjUWoBAABgPEotAAAAjEepBQAAgPEotQAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AAAAMB6lFgAAAMZzeaktLCzUI488ovj4eCUkJGjhwoVnHLtjxw71799fMTExuuWWW7R9+3bHPrvdrvnz56tr165q27athgwZooyMDFdPFwAAADWAy0vtnDlztH37dr311lt67LHHlJSUpDVr1lQYl5eXp5EjRyo+Pl7Lly9XbGysRo0apby8PEnSe++9p4ULF2rq1KlatmyZ6tevrxEjRig/P9/VUwYAAIDhXFpq8/LytHTpUk2ZMkXR0dFKTEzU8OHDtXjx4gpjV69eLR8fH02aNElNmzbVlClTFBAQ4CjAK1as0LBhw3TNNdeocePGmj59uo4dO6bU1FRXThkAAAA1gEtL7c6dO1VcXKzY2FjHtri4OKWnp6u0tNRpbHp6uuLi4uTm5iZJcnNzU9u2bZWWliZJmjRpkm688UbHeDc3N9ntdp04ccKVUwYAAEAN4OnKg9lsNoWEhMjb29uxLSwsTIWFhTp27Jhq167tNPaSSy5xun1oaKh2794tSYqPj3fat3TpUhUXFysuLq7S88nKypLNZnPa5unpr4iIiEof42x4eLg7/R9Vj8ytRf7WIn/rcQ6sRf7Wqm75u7TU5ufnOxVaSY6/FxUVVWrsH8dJZe/qzp49W3fddZfCw8MrPZ8lS5YoKSnJadvYsWM1YcKESh/j7wgO9qvS46MiMrcW+VuL/K3HObAW+VuruuTv0lLr4+NToZSW/93X17dSY/84bsuWLRoxYoSuvPJK3XPPPWc1nwEDBqhr165O2zw9/ZWdnXtWx6ksDw93BQf7KScnXyUlpX99A5wzMrcW+VuL/K3HObAW+VurKvMPCQk469u4tNTWqVNH2dnZKi4ulqdn2aFtNpt8fX0VHBxcYeyRI0ecth05csRpacCmTZs0evRode7cWc8++6zc3c/u7e2IiIgKSw1sthMqLq7aJ35JSWmV3weckbm1yN9a5G89zoG1yN9a1SV/ly6CaNGihTw9PR0f9pKklJQUtWrVqkIhjYmJ0ZYtW2S32yWVXZc2NTVVMTExkqRdu3bp7rvvVpcuXfTCCy/Iy8vLlVMFAABADeLSUuvn56c+ffpo+vTp2rp1q9auXauFCxdq8ODBksretS0oKJAk9ezZUzk5OZo5c6YyMjI0c+ZM5efnq1evXpKkadOmqV69epo8ebKys7Nls9mcbg8AAACUc/nH1SZPnqzo6GgNGTJEjz/+uMaPH6/u3btLkhISErR69WpJUmBgoF599VWlpKSob9++Sk9P1/z58+Xv7y+bzaYtW7YoIyNDV199tRISEhz/ld8eAAAAKOdmL//5/wXCZqu669x6erorJCRA2dm51WJtyYWAzK1F/tYif+txDqxF/taqyvzDw4PO+jbV48JiAAAAwDmg1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsAAADjUWoBAABgPEotAAAAjEepBQAAgPEotQAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AAAAMB6lFgAAAMaj1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsAAADjUWoBAABgPEotAAAAjEepBQAAgPEotQAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AAAAMB6lFgAAAMaj1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwnstLbWFhoR555BHFx8crISFBCxcuPOPYHTt2qH///oqJidEtt9yi7du3O+3/+OOP1a1bN8XExGjs2LE6evSoq6cLAACAGsDlpXbOnDnavn273nrrLT322GNKSkrSmjVrKozLy8vTyJEjFR8fr+XLlys2NlajRo1SXl6eJGnr1q2aMmWKxo0bpyVLlignJ0eTJ0929XQBAABQA7i01Obl5Wnp0qWaMmWKoqOjlZiYqOHDh2vx4sUVxq5evVo+Pj6aNGmSmjZtqilTpiggIMBRgBctWqRevXqpT58+uuyyyzRnzhx98cUX2r9/vyunDAAAgBrApaV2586dKi4uVmxsrGNbXFyc0tPTVVpa6jQ2PT1dcXFxcnNzkyS5ubmpbdu2SktLc+yPj493jK9Xr54iIyOVnp7uyikDAACgBvB05cFsNptCQkLk7e3t2BYWFqbCwkIdO3ZMtWvXdhp7ySWXON0+NDRUu3fvliRlZWUpIiKiwv7Dhw9Xej5ZWVmy2WxO2zw9/Ssc11U8PNwd/y8uLdWYJVu15cDxKrkvAACA88HTw013d26kwR0aOm3/fe+pDlxaavPz850KrSTH34uKiio1tnxcQUHBn+6vjCVLligpKclp29ixYzVhwoRKH+PvCA7205GThUo9cFwlpfYqvS8AAICqVFxq16b9x3VPz4DT7g8O9jvPMzo9l5ZaHx+fCqWz/O++vr6VGls+7kz7/fwqH9yAAQPUtWtXp22env7Kzs6t9DHOhoeHu4KD/ZSTky+PklK9NjBG2w7mVMl9oYybu7t8fT1VUFAs+x+WuKDqkb+1yN96nANrkf/54enhrmsvDavQn37fe0pKXJt/SMjpC/SfcWmprVOnjrKzs1VcXCxPz7JD22w2+fr6Kjg4uMLYI0eOOG07cuSIY2nAmfaHh4dXej4REREVlhrYbCdUXFy1T/ySklIVF5eqVd1gtaob/Nc3wN/m6emukJAAZWfnVvl5RUXkby3ytx7nwFrkf36dKePy3mM1ly6CaNGihTw9PR0f9pKklJQUtWrVSu7uzncVExOjLVu2yG4v+/G83W5XamqqYmJiHPtTUlIc4w8dOqRDhw459gMAAADlXFpq/fz81KdPH02fPl1bt27V2rVrtXDhQg0ePFhS2bu2BQUFkqSePXsqJydHM2fOVEZGhmbOnKn8/Hz16tVLkjRw4EB9+OGHWrp0qXbu3KlJkybp6quvVoMGDVw5ZQAAANQALv+42uTJkxUdHa0hQ4bo8ccf1/jx49W9e3dJUkJCglavXi1JCgwM1KuvvqqUlBT17dtX6enpmj9/vvz9/SVJsbGxeuKJJzRv3jwNHDhQtWrV0qxZs1w9XQAAANQAbvbyn/9fIGy2E1V2bNb2nH9kbi3ytxb5W49zYC3yt1ZV5h8eHnTWt6keFxYDAAAAzgGlFgAAAMaj1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsAAADjUWoBAABgPEotAAAAjEepBQAAgPEotQAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AAAAMB6lFgAAAMaj1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsAAADjUWoBAABgPEotAAAAjEepBQAAgPEotQAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AAAAMB6lFgAAAMaj1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxXFpq7Xa75s6dq44dO6p9+/aaM2eOSktLzzh+//79Gjp0qNq0aaPevXvrq6++ctq/bNky9ezZU7Gxserfv79SUlJcOV0AAADUEC4ttW+88YY+/vhjJSUl6cUXX9SqVav0xhtvnHas3W7X2LFjFRYWpmXLlummm27SuHHj9Msvv0iSkpOT9cQTT2jMmDFauXKlOnfurJEjRyozM9OVUwYAAEAN4NJS+/bbb2vChAmKj49Xx44d9eCDD2rx4sWnHbtx40bt379fTzzxhJo2bapRo0apTZs2WrZsmSRpxYoV6tOnj2688UZFRUXp3nvvVVhYmL744gtXThkAAAA1gKerDpSZmalDhw6pXbt2jm1xcXE6ePCgsrKyFBER4TQ+PT1dLVu2lL+/v9P4tLQ0SdLw4cMVEBBQ4X5OnDjhqikDAACghnBZqbXZbJLkVF7DwsIkSYcPH65Qam02W4VtoaGhOnz4sCQpOjraaV9ycrL27t2rjh07VnpOWVlZjnmV8/T0r3C/ruLh4e70f1Q9MrcW+VuL/K3HObAW+VuruuV/VqW2oKDgjGta8/LyJEne3t6ObeV/LioqqjA+Pz/faWz5+NON3bdvnyZPnqwbbrihQtn9M0uWLFFSUpLTtrFjx2rChAmVPsbfERzsV6XHR0Vkbi3ytxb5W49zYC3yt1Z1yf+sSm16eroGDx582n0TJ06UVFZgfXx8HH+WJD+/ig/Wx8dHx44dc9pWVFQkX19fp2179uzRnXfeqQYNGmjGjBlnM10NGDBAXbt2ddrm6emv7OzcszpOZXl4uCs42E85OfkqKTnzVR/gOmRuLfK3Fvlbj3NgLfK3VlXmHxJScQnqXzmrUtuhQwf98MMPp92XmZmpZ555RjabTfXr15f025KE8PDwCuPr1KmjjIwMp21HjhxxWhqwe/duDR06VA0aNNCCBQsqFN6/EhERcZplDydUXFy1T/ySktIqvw84I3Nrkb+1yN96nANrkb+1qkv+LlsEUadOHUVGRjpdSzYlJUWRkZGnXcMaExOj7777TgUFBU7jY2JiJJWthx02bJiioqL0+uuvKzAw0FVTBQAAQA3jsg+KSdLAgQM1d+5c1a1bV5L07LPPatiwYY79R48elY+PjwICAtS+fXvVq1dPkydP1pgxY7R+/Xpt3bpVs2bNkiTNnj1bpaWlmjlzpvLy8hxrdv39/U97VQQAAABcuFxaau+66y79+uuvGjdunDw8PNSvXz8NHTrUsb9fv366+eabNX78eHl4eOjll1/WlClksfRCAAAgAElEQVRT1LdvX0VFRWnevHmKjIyU3W7X2rVrVVBQoJ49ezrdx7hx4zR+/HhXThsAAACGc7Pb7XarJ3E+2WxVd51bT093hYQEKDs7t1qsLbkQkLm1yN9a5G89zoG1yN9aVZl/eHjQWd+melxYDAAAADgHlFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsAAADjUWoBAABgPEotAAAAjEepBQAAgPEotQAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AAAAMB6lFgAAAMaj1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsAAADjUWoBAABgPEotAAAAjEepBQAAgPEotQAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AAAAMB6lFgAAAMaj1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxnNpqbXb7Zo7d646duyo9u3ba86cOSotLT3j+P3792vo0KFq06aNevfura+++uq049LT09WiRQsdOHDAldMFAABADeHSUvvGG2/o448/VlJSkl588UWtWrVKb7zxxmnH2u12jR07VmFhYVq2bJluuukmjRs3Tr/88ovTuFOnTunRRx/903IMAACAC5tLS+3bb7+tCRMmKD4+Xh07dtSDDz6oxYsXn3bsxo0btX//fj3xxBNq2rSpRo0apTZt2mjZsmVO4xYsWKDAwEBXThMAAAA1jMtKbWZmpg4dOqR27do5tsXFxengwYPKysqqMD49PV0tW7aUv7+/0/i0tDTH3/fs2aPFixfr4YcfdtU0AQAAUAN5uupANptNkhQREeHYFhYWJkk6fPiw0/by8X/cFhoaqsOHD0sqW54wbdo0jR8/XqGhoX9rTllZWY55lfP09K9wv67i4eHu9H9UPTK3Fvlbi/ytxzmwFvlbq7rlf1altqCgQJmZmafdl5eXJ0ny9vZ2bCv/c1FRUYXx+fn5TmPLx5eP/eCDD3Tq1Cn94x//0MGDB89mmg5LlixRUlKS07axY8dqwoQJf+t4lRUc7Felx0dFZG4t8rcW+VuPc2At8rdWdcn/rEptenq6Bg8efNp9EydOlFRWYH18fBx/liQ/v4oP1sfHR8eOHXPaVlRUJF9fX9lsNj3//PN688035ebmdjZTdDJgwAB17drVaZunp7+ys3P/9jH/jIeHu4KD/ZSTk6+SEj7Ydj6QubXI31rkbz3OgbXI31pVmX9ISMBZ3+asSm2HDh30ww8/nHZfZmamnnnmGdlsNtWvX1/Sb0sSwsPDK4yvU6eOMjIynLYdOXJEERER+uqrr5Sdna0BAwZIKluKIEnXX3+9Ro8erdGjR1dqvhEREadZ9nBCxcVV+8QvKSmt8vuAMzK3Fvlbi/ytxzmwFvlbq7rk77I1tXXq1FFkZKRSUlIcpTYlJUWRkZGnXcMaExOj+fPnq6CgQL6+vo7xcXFxSkxMVNu2bR1jMzMzNWjQIM2fP1+XXnqpq6YMAACAGsJlpVaSBg4cqLlz56pu3bqSpGeffVbDhg1z7D969Kh8fHwUEBCg9u3bq169epo8ebLGjBmj9evXa+vWrZo1a5YCAwOdLuPl4eEhSYqMjNRFF13kyikDAACgBnBpqb3rrrv066+/aty4cfLw8FC/fv00dOhQx/5+/frp5ptv1vjx4+Xh4aGXX35ZU6ZMUd++fRUVFaV58+YpMjLSlVMCAADABcDNXr5g9QJhs52osmN7erorJCRA2dm51WJtyYWAzK1F/tYif+txDqxF/taqyvzDw4PO+jbV48JiAAAAwDmg1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsAAADjUWoBAABgPEotAAAAjEepBQAAgPEotQAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AAAAMB6lFgAAAMaj1AIAAMB4lFoAAAAYj1ILAAAA41FqAQAAYDxKLQAAAIxHqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsAAADjUWoBAABgPEotAAAAjEepBQAAgPEotQAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAOO52e12u9WTqCmysrK0ZMkSDRgwQBEREVZP54JA5tYif2uRv/U4B9Yif2tVt/x5p9aFbDabkpKSZLPZrJ7KBYPMrUX+1iJ/63EOrEX+1qpu+VNqAQAAYDxKLQAAAIxHqQUAAIDxPKZPnz7d6knUJAEBAWrfvr0CAgKsnsoFg8ytRf7WIn/rcQ6sRf7Wqk75c/UDAAAAGI/lBwAAADAepRYAAADGo9QCAADAeJRaAAAAGI9SCwAAAONRagEAAGA8Si0AAACMR6kFAACA8Si1AP4Sv6PFOqWlpVZP4YLF8x4wC6W2ko4ePSqJF7nz6eTJk1ZP4YL25ptvasaMGZIkNzc3i2dz4Vm6dKnGjBnD14GFCgsLrZ7CBS07O1sS33etYmLv8bR6AtWZ3W6Xm5ubXnvtNR09elT33HOPfH19rZ5Wjbd582Y9//zz8vX1VcuWLXXzzTerSZMmKi0tlbs7/w6rav/73/80depU5ebm6qGHHrJ6OhecTZs2adq0adq3b58kKScnR8HBwRbP6sKSmpqqF154QbVq1VKLFi3Us2dPNWnSxPE9AVWnPONXX31Vubm5Gjt2rHx8fKye1gXD9N5DQ/gTJSUlkqSNGzfqyy+/VGpqqsUzqvnWr1+vBx54QJ06dVKnTp2UlpamOXPmSBKF9jx48sknNXjwYPXr10+ff/65brjhBqundMHIysrSsGHDNGLECN1888166aWXFB0drfr16xv1TonpNm/erIceekhxcXGKjY1Vamqqhg0bpszMTArtefD777sbNmzQli1bLJ7RhcX03kNL+BOenp46efKkDhw4oJMnT2rdunU6cuSIJLPejjdB+RfS5s2bFR0drXHjxmn48OHq2rWrCgsLlZeXJ4ncq0r5uk0fHx+1bNlSI0aMkJ+fX4Vx5F91XnrpJZWUlOjTTz/V6NGjFRERoczMTP3yyy+UqfOg/GsgNTVVgYGBmjBhgoYNG6YFCxbI399fM2bM0K+//mrxLGu+33/fPX78uDZs2ODIndefqmd676HU/s7hw4crbFu+fLlq1aqlQYMGadu2bfr6668lscbQVcoz9/DwUGlpqTIzM9WuXTvH/u3bt8vLy0s//fSTJHJ3tfL8y98FT0xMlCSlp6dLktatW6dFixbp22+/VWlpKfm72O9fc6ZMmaK33npL9erVkyRlZmYqMDBQoaGhRnwzMdXvvwbsdrt27dqlpk2bys3NzVF0J06cqHXr1ik5OVmnTp2ycro1zt69eyU5fyDygw8+UGhoqAYNGqSUlBRt3LhREq//VaGm9R6P6dOnT7d6ElbbtWuXxowZo6VLl2rdunXy8/NT06ZNJUl79uxRYGCgxowZoy+++EIHDx5Us2bNFBISwvqqc/DHzL28vHTppZeqYcOGat68uWrVqqUXX3xRq1evlo+Pj958802lpKSoffv2CgwMtHr6xvtj/r6+vrrkkkvk4eGhnTt36ueff9Ynn3yiZcuW6cCBA3r33XeVlpamDh06yN/f3+rpG+90rznNmzeXJBUXF8vd3V3e3t56/fXXdfXVVysiIoLXGxc702tQVlaWli9frv79+8vHx0dubm5KS0tTcnKyCgoK1KFDB16DXCQ1NVUTJ07U1VdfreDgYMdzf8+ePapVq5buvvturVu3TocOHdKll16qiy66iK8DF6mpveeCL7WFhYWaNm2a6tevr2HDhikvL08vv/yywsPD1bRpUzVv3lydO3eWJIWEhOg///mPfHx8FBMTU61PbHV2usxfeeUVhYeHq3379qpdu7YkKSgoSEOHDtXtt9+u66+/XklJSQoICFBsbCzZn4M/e87HxcVpz549Sk5Olq+vr1566SX17dtXN954o1588UX5+fmR/zn6s/ybNGkiLy8vSZLNZtO2bdt06aWXqnHjxmTuQmd6DYqIiNB1112n5cuX64cfflBoaKhCQ0P1xhtv6MYbb9TKlSvVunVrNW3atNp/c6/Oyn/q8+mnn+qjjz5SQECAOnTo4PiJUcOGDdWlSxdJZd93P/nkEwUEBKhVq1Zk7gI1ufdc8Fc/2LNnj1JSUrR06VI1btxYCQkJkqRVq1bJz89PPXr0kN1ul91uV6dOndSqVSt98803iomJUWxsLC9sf8OfZe7r66uePXtKklq2bCmpbL1tgwYNNHjwYC1dulTDhw+3bO41wZny//DDDxUeHq6ePXtq3rx56tmzp+NH34GBgbrzzjv1wQcfaMSIERY/ArNV9vl/ySWXyGaz6dChQ5LKvg48PDwsm3dNcqZzsHLlSkVGRuqVV17R1KlTdf/99+vo0aOKj4/XnXfeqb1792rp0qVKTEzkdf8clJfXlJQURUVFKTk5WVdccYXi4+NVWlqqgIAASWXlt3Pnzvr000/15ZdfqnXr1mrdujXfd89RTe49F/ya2sDAQNWrV8/pAwDDhw9XcHCwPvvsM8cnXss/yDR48GDl5OToiy++UG5ubrU9sdXZn2X++eefKzMzU9Jv14gs/0YeHR0tSXxY4xydKf+LLrpIH3zwgcLCwrR+/XqNGjVKbm5ujud4q1atVFxcLJvNZtXUa4TKPv+lsjXOq1atkiQKrQv92dfAv//9b4WGhmrx4sVKSkrSihUr9Oabb0qSatWqpcDAQBUVFVk085ojLS1Nu3bt0ujRo9W4cWMtXrxY0umvcjN06FAdPXpUycnJysvL4/vuOarJveeCKbVnehEqKipSaGiovvvuO8cJDA0NVY8ePfTrr79qw4YNkiQvLy+VlpaqcePG6tatm7755ht98cUX52v6RjrXzN99912tX79ex48fl1R2ua+2bdsqNDT0vMzfdH83/48++ki1a9fWvn37tG3bNsdx1q1bp/j4eIWHh5+3x2Cyc33+S1J8fLw8PT2VkpIiid8udrb+zjk4evSo/vOf/8jNzU3169eXj4+PcnJyJEk//fSTGjduLG9v7/P2GEz2Z+U/JCREl112mW666SZ16NBBBw8edPwDrvx5Xv7hvSZNmigxMVHJycmODy3hz/3Zh0trcu+p8aX21KlTeuKJJ5SUlCSp4jeFJk2aqGHDhtq4caPjYudS2TskYWFh2r59u44dO+Z0m1tvvVW1atVSrVq1qv4BGOhcM9+2bZukst9mMnnyZI0YMUJ33323Vq1apWuvvfb8PRBDnUv+ERER+u6771RYWKjFixfr9ttv1/jx4zVmzBitXLlS3bp1O6+PxUSueM0p/00+DRs2lL+/v9atWye73c61mivpXM5BeHi4duzYoaKiIm3cuFH333+/Jk2apHHjxmnbtm266qqrzutjMdFf5W+32xUVFaV//vOfcnd3V5cuXdSiRQstX75cJ0+edJTZ3xs4cKACAwP5kF4llJaWOspq+d9/ryb3nhr9QbEFCxZo1KhRSk1NVWlpqfr16+f0tnlRUZE8PDzUqFEjLVy4UGFhYbr00kvl6ekpd3d3ubm5adGiRbrjjjvk7e3tuMSLj4+PevXqpUaNGln34KopV2T+zjvv6LbbblOXLl0UFxenoKAgBQcH65lnnnEsQcDpuSr/QYMG6eqrr1ZUVJTc3NwUHBys559/XpdffrmFj676c+Vrjo+Pj2rXrq20tDRFRkaqVatWlNpKcNXXwB133KEWLVooMjJShYWF8vX11dy5c3nd/wt/lX9JSYnTsqbi4mIFBwfLbrcrJSVFx48fV3x8vCQ5xpWUlMjX11e9e/dWVFSUJY/LFG+99ZaefvppffPNN/r111/VunVrubm5OdbB1vTeUyM/KPbf//5Xs2bNUklJiZ577jlt2rTJcW3BkpISubu7q7S01PEjpMaNG6tPnz765JNP1KBBA8e/xFu1aqXc3Fzt379fLVq0kPTbep/yTyijjCszz8/PV0ZGhqKjoxUbG6vY2FjLHpcpXJl/Xl6edu/ercsvv1y9e/dW7969LXtcpnD1a86BAwccrzkPPfQQP+6uBFd/Dfz444+Kjo5WQkKC44M0OLPK5l++Ntxmsyk8PNzxPbV9+/ZKT0/Xhg0b1KNHDzVq1Mjx4cjy2/B998zsdrv++c9/as2aNY5/VLz//vsqKSnRkCFDHO/W1vTeU+P+2X/8+HHNnTtX1113nT777DNdffXV8vDwcLzF7uHhITc3N3l4eOjQoUMaNGiQbr/9dt1xxx0KDw/Xu+++q+TkZEllX6SXXXaZ49ptOL2qyLxZs2ZWPiSjVEX+l156qZUPyShV/ZpDof1rvAZZ62zzHzJkiEaNGqWCggLHUoPAwEBdc8018vf310svveS4Hc7s90s0SkpKlJ6eroEDB+rmm2/WQw89pKioKMcHT8v/cVDTe0+Neac2MzNTderUUa1atbRy5Ur5+Pg49v3666+OD7ecOnVKXl5eWr58uWbMmKFrrrlGY8aMUe3atTV27FitWLFCY8eOVcuWLbVz50498MADfFM5AzK3Fvlbi/ytxzmw1rnk//jjj8vX11fSb7+pKiYmRl26dHFc0qs6XzqqOsjKylKdOnUcf87Pz3f8cpzAwEAdOnRInTp10tGjR1W7dm198MEHeuqpp2r089/NXgN+/+K//vUvbd++XfPmzXPaXv6jizfffFPvv/++Vq9e7dj3zTffKCAgQDExMRWOt3XrVv38889q166d6tatW+XzNxGZW4v8rUX+1uMcWMvV+ZcXWK7HXDmny//hhx/Wpk2b1KxZM23ZskVBQUGy2+3y9vbWtGnT5OPjI29vb7Vu3brC8WrK879GvFP7zTffaM+ePfryyy/VpUsXlZaWyt3d3fGFERQUpLp16+rgwYO6+OKLJUlXXHFFheOUf1GVX+AZZ0bm1iJ/a5G/9TgH1nJV/uXK35Gl0FbOH/OXytbff/vtt5o3b5569+6tadOm6eDBg1q6dKkeeOABLVu2zHEuytW057/xa2ozMzP1888/q169enrnnXdUVFTkWKNT/iZ0s2bNlJ6eLk/Psg5/pjen+TFH5ZC5tcjfWuRvPc6BtVyZP87e6fKXyq792717d8XGxqpr167y8PBQw4YN9cADD8jf31+ffPKJJOdLfNW0579RpfbHH390XAS7/Ivno48+UsOGDXX77bcrLy9P7777rmN/+WUsWrdurXr16unDDz907EPlkLm1yN9a5G89zoG1yN9aZ5O/VFZY169f78i7vPC2atVK+/fvl3T639pWUxjxyDZs2KArr7xS9957r/r06aPPPvtMx48fl5ubm7y9vdWjRw9169ZNrVq10qeffqpDhw45Lh9Sfl227t27a9u2bWe8sDOckbm1yN9a5G89zoG1yN9afyd/qexqKVdddZXmzJmjwsJCeXt7Kzc3VwcPHrwgLk1XbX/5Qvm/+AoKCjR16lT16tVL999/v/Lz87V27Vr98ssvuuKKK9SmTRvFxMTI29tbXl5e2rp1qw4cOKCEhASnNTpZWVnavHmzgoOD1axZsxr3lrsrkLm1yN9a5G89zoG1yN9arshfklq3bq2lS5fq/fff144dOzR//nz5+flp0KBBCgoKsvhRVq1q+U5tcXGx48n//fff6+DBg+rXr58aNmyohx56SD179tRXX32lNWvWSPrt7fW4uDh17NhRmzdvVmpqqiQ5Lv7cuXNn1apVS2FhYRY8ouqPzK1F/tYif+txDqxF/tZyRf4pKSmSpPDwcC1YsEB33nmnfH191bdvXy1evFj16tWz5sGdR9Xu6gevvfaavvzyS9WpU0dXXnml2rdvr9zcXOXn5zvGJCYmas+ePZo/f7569Oghb29vFRcXy9PTU1deeaW2bdumd999V23btpWXl5eKi4tVu3Ztvfjii3yy8jTI3Frkby3ytx7nwFrkby1X5f/ee+8pLi5OkhQVFaWoqKgL7lq/1Wb5QWFhoR555BF9/fXXGjBggPbv36/Vq1crMzNTzZs31/fff++4bEVAQID8/f319ddfKz8/X23atJHdbpe7u7vCwsKUl5enzz77TH5+fmrRooVjUXRNXhz9d5C5tcjfWuRvPc6BtcjfWlWZf7kLqdBK1ajUZmZmavHixXr44YfVo0cPXXPNNfL09NSiRYt0+eWXa//+/WrUqJEiIiIklV0Dz2azKS0tTVdddZX8/PwcC9Rr1aql/Px8xcbGXhBvt/9dZG4t8rcW+VuPc2At8rcW+btetfkn1L59+7Rjxw61a9dOUtkn+EJCQnTxxReradOmOnXqlFatWuUYHxQUpMsuu0z5+fnKy8uT9Nu/CC+++GLdd999io2NPf8PxCBkbi3ytxb5W49zYC3ytxb5u161KbWtW7dW165dtW/fPsdlP7y9vZWRkaHu3burU6dO+u677xzXvJOkyMhIbdmyxelCwqg8MrcW+VuL/K3HObAW+VuL/F2v2nxQLCAgQLNnz5a/v79j27Zt29SkSRPVqlVLvXv31rFjxzR37lxFRESoWbNm+uabb9SlSxeFhoZaOHNzkbm1yN9a5G89zoG1yN9a5O961abUSqpw/bQtW7Y4fhdx3bp1NXr0aB07dkyPPvqo3NzcdOLECc2cOVM+Pj5WTLdGIHNrkb+1yN96nANrkb+1yN/F7NXUvn377DExMfYVK1Y4tp08edJut9vt2dnZ9uTkZKumVmORubXI31rkbz3OgbXI31rkf+6qzZracvb/v65kx44dkqRu3bpJkubPn6+4uDitWLFCF110keMyFzh3ZG4t8rcW+VuPc2At8rcW+btOtVp+IP12TbXdu3erRYsWWrt2rZKSklRaWqp//etfuuaaayyeYc1D5tYif2uRv/U4B9Yif2uRv+tUu1JbzsPDQ1u2bNHOnTt19913a+TIkVZPqcYjc2uRv7XI33qcA2uRv7XI/9y52cvf965m1q1bpx9++EF33XWXvL29rZ7OBYHMrUX+1iJ/63EOrEX+1iL/c1dtS639Avt9xdUBmVuL/K1F/tbjHFiL/K1F/ueu2pZaAAAAoLKq3dUPAAAAgLNFqQUAAIDxKLUAAAAwHqUWAAAAxqPUAgAAwHiUWgAAABiPUgsANVBKSoo2b958Tsd4+OGHNWjQIBfNCACqFqUWAGqg2267Tfv27bN6GgBw3lBqAQAAYDxKLQBUsdzcXD355JNKSEhQbGys7rjjDm3fvl3Lly9XYmKiZsyYobi4OI0ZM0aS9OOPP2rEiBGKjY1VQkKCHnjgAdlsNsfxjh8/rkcffVRdunRRdHS0OnXqpEcffVT5+fmSpObNm0uSJk+erIcffliSlJmZqfvuu0/x8fHq0KGDRo8erb179zqOabfb9fLLL+vKK69UmzZtNHnyZBUWFp6nhADg3FFqAaCK3XvvvUpOTtasWbO0cuVKNWjQQMOGDVNOTo727dunrKwsrVy5Uvfdd58yMzN12223KSoqSh988IFeeeUVnTx5UgMGDFBeXp6ksrWuO3bsUFJSkj799FNNnjxZK1eu1JIlSyRJX331lSTpkUce0ZQpU5SXl+dYG7to0SK98847CgkJ0T/+8Q9lZmZKkubPn68FCxZo0qRJWr58uYKDg7V69WoL0gKAv8fT6gkAQE32008/KTk5Wa+//roSEhIkSdOnT1dwcLD8/f0lSWPGjFGDBg0kSS+88ILq1q2rRx991HGMF154QR07dtSaNWvUt29fde7cWe3atXO8I1u/fn0tWrRIu3btkiSFh4dLkoKCghQUFKSlS5cqJydHzzzzjDw9y172Z86cqU2bNun999/XuHHj9M4772jw4MG6/vrrJZW9y7tp06bzkBAAuAalFgCqUHnRbNOmjWObj4+PJk+erOXLl0uSGjVq5Ni3Y8cO7d69W7GxsU7HKSws1I8//iip7ENg69at04oVK7R3715lZGTowIEDatKkyWnnsGPHDh0/flzt2rU77TGzs7Nls9nUqlUrp/1t2rRx3CcAVHeUWgCoQuXvjP4ZX19fx59LS0vVsWNHPfbYYxXGBQUF/b927teluT0O4Phb1DKQIWqYwTHUlQVhzSCIwSCyP8AuNlEZomHBYhgMFIMGwTBMRoOgYFhREAWd6FTGwIEGBcPa0D03XO7gQQz3og/3wPsVz4/vOXzT+8CHQ6PRYGZmhoeHByYnJ5mYmCCRSJDJZL5cv9FoEIvF2Nzc/HQuFArR0tIC/D1X+2/fXZL+L5yplaQf1N/fD0CxWGwee39/Z2xsjLe3t0/XDw4OUi6XiUQiRKNRotEo4XCY1dVV7u/vub29pVAosL6+TjqdJpVK0dfXx+Pj46co/Uc8Hufp6YmOjo7mmr29veRyOc7Ozujs7CQSiXB+fv7bfdfX19+4E5L0s4xaSfpBsViM8fFxVlZWOD09pVKpkMlkvvyzwNTUFLVajXQ6TalUolQqMT8/T7FYJB6P093dTVtbGwcHB1SrVYrFInNzc7y8vFCv15vrhEKh5mhBKpUiHA4zOzvL5eUl5XKZpaUlCoVCcy53enqa3d1d9vb2qFQqrK2tcXV19Uf2SJK+Q8uvrz7tJUnfolarkc1mOTw8pF6vMzQ01PyDwfLyMnd3d79df3NzQy6X4+LigtbWVpLJJIuLiwwMDACwv7/PxsYGz8/P9PT0MDo6SgGHL18AAACnSURBVHt7O8fHxxwdHQGwsbHB9vY2w8PDbG1tUa1WyWaznJyc8PHxQSKRYGFhgWQy2XxuPp9nZ2eH19dXRkZG6OrqolKpkM/n/9xmSdJ/ZNRKkiQp8Bw/kCRJUuAZtZIkSQo8o1aSJEmBZ9RKkiQp8IxaSZIkBZ5RK0mSpMAzaiVJkhR4Rq0kSZICz6iVJElS4Bm1kiRJCjyjVpIkSYFn1EqSJCnw/gKKbQ7Oj9WzeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e351470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({\n",
    "    'created': data_set.created,\n",
    "    'youtube_favorites': data_set.youtube_favorite_count,\n",
    "})\n",
    "plot_data_set.plot(x='created');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics Analysis <a class=\"anchor\" id=\"statistics-analysis\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Views <a class=\"anchor\" id=\"views\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10de219b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({\n",
    "    'created': data_set.created,\n",
    "    'view_count': data_set.view_count,\n",
    "})\n",
    "plot_data_set.plot(x='created');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Likes <a class=\"anchor\" id=\"likes\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df8efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({\n",
    "    'created': data_set.created,\n",
    "    'like_count': data_set.like_count,\n",
    "})\n",
    "plot_data_set.plot(x='created');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dislikes <a class=\"anchor\" id=\"dislikes\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10640ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({\n",
    "    'created': data_set.created,\n",
    "    'dislike_count': data_set.dislike_count,\n",
    "})\n",
    "plot_data_set.plot(x='created');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Favorites <a class=\"anchor\" id=\"favorites\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c8e7b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({\n",
    "    'created': data_set.created,\n",
    "    'favorite_count': data_set.favorite_count,\n",
    "})\n",
    "plot_data_set.plot(x='created');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Youtube Statistics Histograms <a class=\"anchor\" id=\"youtube-statistics-histograms\"></a>\n",
    "\n",
    "A histogram is an accurate graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable (quantitative variable).\n",
    "\n",
    "Basically, histograms are used to represent data given in form of some groups. X-axis is about bin ranges where Y-axis talks about frequency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Youtube Views Histogram <a class=\"anchor\" id=\"youtube-views-histogram\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cc989b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({'youtube_view_count': data_set.youtube_view_count}, columns=['youtube_view_count'])\n",
    "plot_data_set.hist(bins=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Youtube Like Histogram <a class=\"anchor\" id=\"youtube-likes-histogram\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105f4c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({'youtube_like_count': data_set.youtube_like_count}, columns=['youtube_like_count'])\n",
    "plot_data_set.hist(bins=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Youtube Dislike Histogram <a class=\"anchor\" id=\"youtube-dislikes-histogram\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10db3f0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({'youtube_dislike_count': data_set.youtube_dislike_count}, columns=['youtube_dislike_count'])\n",
    "plot_data_set.hist(bins=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Youtube Favorite Histogram <a class=\"anchor\" id=\"youtube-favorites-histogram\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1091bb9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({'youtube_favorite_count': data_set.youtube_favorite_count}, columns=['youtube_favorite_count'])\n",
    "plot_data_set.hist(bins=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics Histograms <a class=\"anchor\" id=\"statistics-histogram\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Histogram <a class=\"anchor\" id=\"views-histogram\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ea0bb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({'view_count': data_set.view_count}, columns=['view_count'])\n",
    "plot_data_set.hist(bins=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Likes Histogram <a class=\"anchor\" id=\"likes-histogram\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cc98e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({'like_count': data_set.like_count}, columns=['like_count'])\n",
    "plot_data_set.hist(bins=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dislikes Histogram <a class=\"anchor\" id=\"dislikes-histogram\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c527438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({'dislike_count': data_set.dislike_count}, columns=['dislike_count'])\n",
    "plot_data_set.hist(bins=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Favorites Histogram <a class=\"anchor\" id=\"favorites-histogram\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e2080f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_set = pd.DataFrame({'favorite_count': data_set.favorite_count}, columns=['favorite_count'])\n",
    "plot_data_set.hist(bins=150);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}